#+title: =CORTEX=

 #+author: Robert McIntyre

 #+email: rlm@mit.edu

 #+description: Using embodied AI to facilitate Artificial Imagination.

 #+keywords: AI, clojure, embodiment

 #+LaTeX_CLASS_OPTIONS: [nofloat]

 

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 * Empathy \& Embodiment: problem solving strategies

 

   By the time you have read this thesis, you will understand a novel

   approach to representing and recognizing physical actions using

   embodiment and empathy. You will also see one way to efficiently

   implement physical empathy for embodied creatures. Finally, you will

   become familiar with =CORTEX=, a system for designing and simulating

   creatures with rich senses, which I have designed as a library that

   you can use in your own research. Note that I /do not/ process video

   directly --- I start with knowledge of the positions of a creature's

   body parts and work from there.

   

   This is the core vision of my thesis: That one of the important ways

   in which we understand others is by imagining ourselves in their

   position and empathically feeling experiences relative to our own

   bodies. By understanding events in terms of our own previous

   corporeal experience, we greatly constrain the possibilities of what

   would otherwise be an unwieldy exponential search. This extra

   constraint can be the difference between easily understanding what

   is happening in a video and being completely lost in a sea of

   incomprehensible color and movement.

   

 ** The problem: recognizing actions is hard!

    

    Examine figure \ref{cat-drink}. What is happening? As you, and

    indeed very young children, can easily determine, this is an image

    of drinking.

 

    #+caption: A cat drinking some water. Identifying this action is 

    #+caption: beyond the capabilities of existing computer vision systems.

    #+name: cat-drink

    #+ATTR_LaTeX: :width 7cm

    [[./images/cat-drinking.jpg]]

      

    Nevertheless, it is beyond the state of the art for a computer

    vision program to describe what's happening in this image. Part of

    the problem is that many computer vision systems focus on

    pixel-level details or comparisons to example images (such as

    \cite{volume-action-recognition}), but the 3D world is so variable

    that it is hard to describe the world in terms of possible images.

 

    In fact, the contents of a scene may have much less to do with

    pixel probabilities than with recognizing various affordances:

    things you can move, objects you can grasp, spaces that can be

    filled . For example, what processes might enable you to see the

    chair in figure \ref{hidden-chair}?

 

    #+caption: The chair in this image is quite obvious to humans, but 

    #+caption: it can't be found by any modern computer vision program.

    #+name: hidden-chair

    #+ATTR_LaTeX: :width 10cm

    [[./images/fat-person-sitting-at-desk.jpg]]

 

    Finally, how is it that you can easily tell the difference between

    how the girl's /muscles/ are working in figure \ref{girl}?

    

    #+caption: The mysterious ``common sense'' appears here as you are able 

    #+caption: to discern the difference in how the girl's arm muscles

    #+caption: are activated between the two images. When you compare

    #+caption: these two images, do you feel something in your own arm

    #+caption: muscles?

    #+name: girl

    #+ATTR_LaTeX: :width 7cm

    [[./images/wall-push.png]]

   

    Each of these examples tells us something about what might be going

    on in our minds as we easily solve these recognition problems:

    

    - The hidden chair shows us that we are strongly triggered by cues

      relating to the position of human bodies, and that we can

      determine the overall physical configuration of a human body even

      if much of that body is occluded.

 

    - The picture of the girl pushing against the wall tells us that we

      have common sense knowledge about the kinetics of our own bodies.

      We know well how our muscles would have to work to maintain us in

      most positions, and we can easily project this self-knowledge to

      imagined positions triggered by images of the human body.

 

    - The cat tells us that imagination of some kind plays an important

      role in understanding actions. The question is: Can we be more

      precise about what sort of imagination is required to understand

      these actions?

 

 ** A step forward: the sensorimotor-centered approach

 

    In this thesis, I explore the idea that our knowledge of our own

    bodies, combined with our own rich senses, enables us to recognize

    the actions of others.

 

    For example, I think humans are able to label the cat video as

    ``drinking'' because they imagine /themselves/ as the cat, and

    imagine putting their face up against a stream of water and

    sticking out their tongue. In that imagined world, they can feel

    the cool water hitting their tongue, and feel the water entering

    their body, and are able to recognize that /feeling/ as drinking.

    So, the label of the action is not really in the pixels of the

    image, but is found clearly in a simulation / recollection inspired

    by those pixels. An imaginative system, having been trained on

    drinking and non-drinking examples and learning that the most

    important component of drinking is the feeling of water flowing

    down one's throat, would analyze a video of a cat drinking in the

    following manner:

    

    1. Create a physical model of the video by putting a ``fuzzy''

       model of its own body in place of the cat. Possibly also create

       a simulation of the stream of water.

 

    2. Play out this simulated scene and generate imagined sensory

       experience. This will include relevant muscle contractions, a

       close up view of the stream from the cat's perspective, and most

       importantly, the imagined feeling of water entering the mouth.

       The imagined sensory experience can come from a simulation of

       the event, but can also be pattern-matched from previous,

       similar embodied experience.

 

    3. The action is now easily identified as drinking by the sense of

       taste alone. The other senses (such as the tongue moving in and

       out) help to give plausibility to the simulated action. Note that

       the sense of vision, while critical in creating the simulation,

       is not critical for identifying the action from the simulation.

 

    For the chair examples, the process is even easier:

 

     1. Align a model of your body to the person in the image.

 

     2. Generate proprioceptive sensory data from this alignment.

   

     3. Use the imagined proprioceptive data as a key to lookup related

        sensory experience associated with that particular proprioceptive

        feeling.

 

     4. Retrieve the feeling of your bottom resting on a surface, your

        knees bent, and your leg muscles relaxed.

 

     5. This sensory information is consistent with your =sitting?=

        sensory predicate, so you (and the entity in the image) must be

        sitting.

 

     6. There must be a chair-like object since you are sitting.

 

    Empathy offers yet another alternative to the age-old AI

    representation question: ``What is a chair?'' --- A chair is the

    feeling of sitting!

 

    One powerful advantage of empathic problem solving is that it

    factors the action recognition problem into two easier problems. To

    use empathy, you need an /aligner/, which takes the video and a

    model of your body, and aligns the model with the video. Then, you

    need a /recognizer/, which uses the aligned model to interpret the

    action. The power in this method lies in the fact that you describe

    all actions from a body-centered viewpoint. You are less tied to

    the particulars of any visual representation of the actions. If you

    teach the system what ``running'' is, and you have a good enough

    aligner, the system will from then on be able to recognize running

    from any point of view -- even strange points of view like above or

    underneath the runner. This is in contrast to action recognition

    schemes that try to identify actions using a non-embodied approach.

    If these systems learn about running as viewed from the side, they

    will not automatically be able to recognize running from any other

    viewpoint.

 

    Another powerful advantage is that using the language of multiple

    body-centered rich senses to describe body-centered actions offers

    a massive boost in descriptive capability. Consider how difficult

    it would be to compose a set of HOG (Histogram of Oriented

    Gradients) filters to describe the action of a simple worm-creature

    ``curling'' so that its head touches its tail, and then behold the

    simplicity of describing thus action in a language designed for the

    task (listing \ref{grand-circle-intro}):

 

    #+caption: Body-centered actions are best expressed in a body-centered 

    #+caption: language. This code detects when the worm has curled into a 

    #+caption: full circle. Imagine how you would replicate this functionality

    #+caption: using low-level pixel features such as HOG filters!

    #+name: grand-circle-intro

    #+begin_listing clojure

    #+begin_src clojure

 (defn grand-circle?

   "Does the worm form a majestic circle (one end touching the other)?"

   [experiences]

   (and (curled? experiences)

        (let [worm-touch (:touch (peek experiences))

              tail-touch (worm-touch 0)

              head-touch (worm-touch 4)]

          (and (< 0.2 (contact worm-segment-bottom-tip tail-touch))

               (< 0.2 (contact worm-segment-top-tip    head-touch))))))

    #+end_src

    #+end_listing

 

 ** =EMPATH= recognizes actions using empathy

 

    Exploring these ideas further demands a concrete implementation, so

    first, I built a system for constructing virtual creatures with

    physiologically plausible sensorimotor systems and detailed

    environments. The result is =CORTEX=, which I describe in chapter

    \ref{sec-2}.

 

    Next, I wrote routines which enabled a simple worm-like creature to

    infer the actions of a second worm-like creature, using only its

    own prior sensorimotor experiences and knowledge of the second

    worm's joint positions. This program, =EMPATH=, is described in

    chapter \ref{sec-3}. It's main components are:

 

    - Embodied Action Definitions :: Many otherwise complicated actions

         are easily described in the language of a full suite of

         body-centered, rich senses and experiences. For example,

         drinking is the feeling of water flowing down your throat, and

         cooling your insides. It's often accompanied by bringing your

         hand close to your face, or bringing your face close to water.

         Sitting down is the feeling of bending your knees, activating

         your quadriceps, then feeling a surface with your bottom and

         relaxing your legs. These body-centered action descriptions

         can be either learned or hard coded.

 

    - Guided Play      :: The creature moves around and experiences the

         world through its unique perspective. As the creature moves,

         it gathers experiences that satisfy the embodied action

         definitions. 

 

    - Posture Imitation :: When trying to interpret a video or image,

         the creature takes a model of itself and aligns it with

         whatever it sees. This alignment might even cross species, as

         when humans try to align themselves with things like ponies,

         dogs, or other humans with a different body type.

 

    - Empathy          :: The alignment triggers associations with

         sensory data from prior experiences. For example, the

         alignment itself easily maps to proprioceptive data. Any

         sounds or obvious skin contact in the video can to a lesser

         extent trigger previous experience keyed to hearing or touch.

         Segments of previous experiences gained from play are stitched

         together to form a coherent and complete sensory portrait of

         the scene.

 

    - Recognition :: With the scene described in terms of remembered

         first person sensory events, the creature can now run its

         action-definition programs (such as the one in listing

         \ref{grand-circle-intro}) on this synthesized sensory data,

         just as it would if it were actually experiencing the scene

         first-hand. If previous experience has been accurately

         retrieved, and if it is analogous enough to the scene, then

         the creature will correctly identify the action in the scene.

 

    My program =EMPATH= uses this empathic problem solving technique

    to interpret the actions of a simple, worm-like creature. 

    

    #+caption: The worm performs many actions during free play such as 

    #+caption: curling, wiggling, and resting.

    #+name: worm-intro

    #+ATTR_LaTeX: :width 15cm

    [[./images/worm-intro-white.png]]

 

    #+caption: =EMPATH= recognized and classified each of these 

    #+caption: poses by inferring the complete sensory experience 

    #+caption: from proprioceptive data.

    #+name: worm-recognition-intro

    #+ATTR_LaTeX: :width 15cm

    [[./images/worm-poses.png]]

    

 *** Main Results 

 

    - After one-shot supervised training, =EMPATH= was able to

      recognize a wide variety of static poses and dynamic

      actions---ranging from curling in a circle to wiggling with a

      particular frequency --- with 95\% accuracy.

 

    - These results were completely independent of viewing angle

      because the underlying body-centered language fundamentally is

      independent; once an action is learned, it can be recognized

      equally well from any viewing angle.

 

    - =EMPATH= is surprisingly short; the sensorimotor-centered

      language provided by =CORTEX= resulted in extremely economical

      recognition routines --- about 500 lines in all --- suggesting

      that such representations are very powerful, and often

      indispensable for the types of recognition tasks considered here.

 

    - For expediency's sake, I relied on direct knowledge of joint

      positions in this proof of concept. However, I believe that the

      structure of =EMPATH= and =CORTEX= will make future work to

      enable video analysis much easier than it would otherwise be.

 

 ** =EMPATH= is built on =CORTEX=, a creature builder.

 

    I built =CORTEX= to be a general AI research platform for doing

    experiments involving multiple rich senses and a wide variety and

    number of creatures. I intend it to be useful as a library for many

    more projects than just this thesis. =CORTEX= was necessary to meet

    a need among AI researchers at CSAIL and beyond, which is that

    people often will invent wonderful ideas that are best expressed in

    the language of creatures and senses, but in order to explore those

    ideas they must first build a platform in which they can create

    simulated creatures with rich senses! There are many ideas that

    would be simple to execute (such as =EMPATH= or Larson's

    self-organizing maps (\cite{larson-symbols})), but attached to them

    is the multi-month effort to make a good creature simulator. Often,

    that initial investment of time proves to be too much, and the

    project must make do with a lesser environment or be abandoned

    entirely.

 

    =CORTEX= is well suited as an environment for embodied AI research

    for three reasons:

 

    - You can design new creatures using Blender (\cite{blender}), a

      popular, free 3D modeling program. Each sense can be specified

      using special Blender nodes with biologically inspired

      parameters. You need not write any code to create a creature, and

      can use a wide library of pre-existing blender models as a base

      for your own creatures.

 

    - =CORTEX= implements a wide variety of senses: touch,

      proprioception, vision, hearing, and muscle tension. Complicated

      senses like touch and vision involve multiple sensory elements

      embedded in a 2D surface. You have complete control over the

      distribution of these sensor elements through the use of simple

      image files. =CORTEX= implements more comprehensive hearing than

      any other creature simulation system available.

 

    - =CORTEX= supports any number of creatures and any number of

      senses. Time in =CORTEX= dilates so that the simulated creatures

      always perceive a perfectly smooth flow of time, regardless of

      the actual computational load.

 

    =CORTEX= is built on top of =jMonkeyEngine3=

    (\cite{jmonkeyengine}), which is a video game engine designed to

    create cross-platform 3D desktop games. =CORTEX= is mainly written

    in clojure, a dialect of =LISP= that runs on the Java Virtual

    Machine (JVM). The API for creating and simulating creatures and

    senses is entirely expressed in clojure, though many senses are

    implemented at the layer of jMonkeyEngine or below. For example,

    for the sense of hearing I use a layer of clojure code on top of a

    layer of java JNI bindings that drive a layer of =C++= code which

    implements a modified version of =OpenAL= to support multiple

    listeners. =CORTEX= is the only simulation environment that I know

    of that can support multiple entities that can each hear the world

    from their own perspective. Other senses also require a small layer

    of Java code. =CORTEX= also uses =bullet=, a physics simulator

    written in =C=.

 

    #+caption: Here is the worm from figure \ref{worm-intro} modeled 

    #+caption: in Blender, a free 3D-modeling program. Senses and 

    #+caption: joints are described using special nodes in Blender.

    #+name: worm-recognition-intro-2

    #+ATTR_LaTeX: :width 12cm

    [[./images/blender-worm.png]]

 

    Here are some things I anticipate that =CORTEX= might be used for:

 

    - exploring new ideas about sensory integration

    - distributed communication among swarm creatures

    - self-learning using free exploration, 

    - evolutionary algorithms involving creature construction

    - exploration of exotic senses and effectors that are not possible

      in the real world (such as telekinesis or a semantic sense)

    - imagination using subworlds

 

    During one test with =CORTEX=, I created 3,000 creatures each with

    its own independent senses and ran them all at only 1/80 real time.

    In another test, I created a detailed model of my own hand,

    equipped with a realistic distribution of touch (more sensitive at

    the fingertips), as well as eyes and ears, and it ran at around 1/4

    real time.

 

 #+BEGIN_LaTeX

    \begin{sidewaysfigure}

    \includegraphics[width=9.5in]{images/full-hand.png}

    \caption{

    I modeled my own right hand in Blender and rigged it with all the

    senses that {\tt CORTEX} supports. My simulated hand has a

    biologically inspired distribution of touch sensors. The senses are

    displayed on the right (the red/black squares are raw sensory output), 

    and the simulation is displayed on the

    left. Notice that my hand is curling its fingers, that it can see

    its own finger from the eye in its palm, and that it can feel its

    own thumb touching its palm.}

    \end{sidewaysfigure}

 #+END_LaTeX

 

 * Designing =CORTEX=

 

   In this chapter, I outline the design decisions that went into

   making =CORTEX=, along with some details about its implementation.

   (A practical guide to getting started with =CORTEX=, which skips

   over the history and implementation details presented here, is

   provided in an appendix at the end of this thesis.)

 

   Throughout this project, I intended for =CORTEX= to be flexible and

   extensible enough to be useful for other researchers who want to

   test ideas of their own. To this end, wherever I have had to make

   architectural choices about =CORTEX=, I have chosen to give as much

   freedom to the user as possible, so that =CORTEX= may be used for

   things I have not foreseen.

 

 ** Building in simulation versus reality

    The most important architectural decision of all is the choice to

    use a computer-simulated environment in the first place! The world

    is a vast and rich place, and for now simulations are a very poor

    reflection of its complexity. It may be that there is a significant

    qualitative difference between dealing with senses in the real

    world and dealing with pale facsimiles of them in a simulation

    (\cite{brooks-representation}). What are the advantages and

    disadvantages of a simulation vs. reality?

    

 *** Simulation

 

     The advantages of virtual reality are that when everything is a

     simulation, experiments in that simulation are absolutely

     reproducible. It's also easier to change the creature and

     environment to explore new situations and different sensory

     combinations.

 

     If the world is to be simulated on a computer, then not only do

     you have to worry about whether the creature's senses are rich

     enough to learn from the world, but whether the world itself is

     rendered with enough detail and realism to give enough working

     material to the creature's senses. To name just a few

     difficulties facing modern physics simulators: destructibility of

     the environment, simulation of water/other fluids, large areas,

     nonrigid bodies, lots of objects, smoke. I don't know of any

     computer simulation that would allow a creature to take a rock

     and grind it into fine dust, then use that dust to make a clay

     sculpture, at least not without spending years calculating the

     interactions of every single small grain of dust. Maybe a

     simulated world with today's limitations doesn't provide enough

     richness for real intelligence to evolve.

 

 *** Reality

 

     The other approach for playing with senses is to hook your

     software up to real cameras, microphones, robots, etc., and let it

     loose in the real world. This has the advantage of eliminating

     concerns about simulating the world at the expense of increasing

     the complexity of implementing the senses. Instead of just

     grabbing the current rendered frame for processing, you have to

     use an actual camera with real lenses and interact with photons to

     get an image. It is much harder to change the creature, which is

     now partly a physical robot of some sort, since doing so involves

     changing things around in the real world instead of modifying

     lines of code. While the real world is very rich and definitely

     provides enough stimulation for intelligence to develop (as

     evidenced by our own existence), it is also uncontrollable in the

     sense that a particular situation cannot be recreated perfectly or

     saved for later use. It is harder to conduct Science because it is

     harder to repeat an experiment. The worst thing about using the

     real world instead of a simulation is the matter of time. Instead

     of simulated time you get the constant and unstoppable flow of

     real time. This severely limits the sorts of software you can use

     to program an AI, because all sense inputs must be handled in real

     time. Complicated ideas may have to be implemented in hardware or

     may simply be impossible given the current speed of our

     processors. Contrast this with a simulation, in which the flow of

     time in the simulated world can be slowed down to accommodate the

     limitations of the creature's programming. In terms of cost, doing

     everything in software is far cheaper than building custom

     real-time hardware. All you need is a laptop and some patience.

     

 ** Simulated time enables rapid prototyping \& simple programs

 

    I envision =CORTEX= being used to support rapid prototyping and

    iteration of ideas. Even if I could put together a well constructed

    kit for creating robots, it would still not be enough because of

    the scourge of real-time processing. Anyone who wants to test their

    ideas in the real world must always worry about getting their

    algorithms to run fast enough to process information in real time.

    The need for real time processing only increases if multiple senses

    are involved. In the extreme case, even simple algorithms will have

    to be accelerated by ASIC chips or FPGAs, turning what would

    otherwise be a few lines of code and a 10x speed penalty into a

    multi-month ordeal. For this reason, =CORTEX= supports

    /time-dilation/, which scales back the framerate of the simulation

    in proportion to the amount of processing each frame. From the

    perspective of the creatures inside the simulation, time always

    appears to flow at a constant rate, regardless of how complicated

    the environment becomes or how many creatures are in the

    simulation. The cost is that =CORTEX= can sometimes run slower than

    real time. Time dilation works both ways, however --- simulations

    of very simple creatures in =CORTEX= generally run at 40x real-time

    on my machine!

 

 ** All sense organs are two-dimensional surfaces

 

    If =CORTEX= is to support a wide variety of senses, it would help

    to have a better understanding of what a sense actually is! While

    vision, touch, and hearing all seem like they are quite different

    things, I was surprised to learn during the course of this thesis

    that they (and all physical senses) can be expressed as exactly the

    same mathematical object!

 

    Human beings are three-dimensional objects, and the nerves that

    transmit data from our various sense organs to our brain are

    essentially one-dimensional. This leaves up to two dimensions in

    which our sensory information may flow. For example, imagine your

    skin: it is a two-dimensional surface around a three-dimensional

    object (your body). It has discrete touch sensors embedded at

    various points, and the density of these sensors corresponds to the

    sensitivity of that region of skin. Each touch sensor connects to a

    nerve, all of which eventually are bundled together as they travel

    up the spinal cord to the brain. Intersect the spinal nerves with a

    guillotining plane and you will see all of the sensory data of the

    skin revealed in a roughly circular two-dimensional image which is

    the cross section of the spinal cord. Points on this image that are

    close together in this circle represent touch sensors that are

    /probably/ close together on the skin, although there is of course

    some cutting and rearrangement that has to be done to transfer the

    complicated surface of the skin onto a two dimensional image.

 

    Most human senses consist of many discrete sensors of various

    properties distributed along a surface at various densities. For

    skin, it is Pacinian corpuscles, Meissner's corpuscles, Merkel's

    disks, and Ruffini's endings (\cite{textbook901}), which detect

    pressure and vibration of various intensities. For ears, it is the

    stereocilia distributed along the basilar membrane inside the

    cochlea; each one is sensitive to a slightly different frequency of

    sound. For eyes, it is rods and cones distributed along the surface

    of the retina. In each case, we can describe the sense with a

    surface and a distribution of sensors along that surface.

 

    In fact, almost every human sense can be effectively described in

    terms of a surface containing embedded sensors. If the sense had

    any more dimensions, then there wouldn't be enough room in the

    spinal cord to transmit the information!

 

    Therefore, =CORTEX= must support the ability to create objects and

    then be able to ``paint'' points along their surfaces to describe

    each sense. 

 

    Fortunately this idea is already a well known computer graphics

    technique called /UV-mapping/. In UV-mapping, the three-dimensional

    surface of a model is cut and smooshed until it fits on a

    two-dimensional image. You paint whatever you want on that image,

    and when the three-dimensional shape is rendered in a game the

    smooshing and cutting is reversed and the image appears on the

    three-dimensional object.

 

    To make a sense, interpret the UV-image as describing the

    distribution of that senses' sensors. To get different types of

    sensors, you can either use a different color for each type of

    sensor, or use multiple UV-maps, each labeled with that sensor

    type. I generally use a white pixel to mean the presence of a

    sensor and a black pixel to mean the absence of a sensor, and use

    one UV-map for each sensor-type within a given sense.

 

    #+CAPTION: The UV-map for an elongated icososphere. The white

    #+caption: dots each represent a touch sensor. They are dense 

    #+caption: in the regions that describe the tip of the finger, 

    #+caption: and less dense along the dorsal side of the finger 

    #+caption: opposite the tip.

    #+name: finger-UV

    #+ATTR_latex: :width 10cm

    [[./images/finger-UV.png]]

 

    #+caption: Ventral side of the UV-mapped finger. Note the 

    #+caption: density of touch sensors at the tip.

    #+name: finger-side-view

    #+ATTR_LaTeX: :width 10cm

    [[./images/finger-1.png]]

 

 ** Video game engines provide ready-made physics and shading

    

    I did not need to write my own physics simulation code or shader to

    build =CORTEX=. Doing so would lead to a system that is impossible

    for anyone but myself to use anyway. Instead, I use a video game

    engine as a base and modify it to accommodate the additional needs

    of =CORTEX=. Video game engines are an ideal starting point to

    build =CORTEX=, because they are not far from being creature

    building systems themselves.

    

    First off, general purpose video game engines come with a physics

    engine and lighting / sound system. The physics system provides

    tools that can be co-opted to serve as touch, proprioception, and

    muscles. Because some games support split screen views, a good

    video game engine will allow you to efficiently create multiple

    cameras in the simulated world that can be used as eyes. Video game

    systems offer integrated asset management for things like textures

    and creature models, providing an avenue for defining creatures.

    They also understand UV-mapping, because this technique is used to

    apply a texture to a model. Finally, because video game engines

    support a large number of developers, as long as =CORTEX= doesn't

    stray too far from the base system, other researchers can turn to

    this community for help when doing their research.

    

 ** =CORTEX= is based on jMonkeyEngine3

 

    While preparing to build =CORTEX= I studied several video game

    engines to see which would best serve as a base. The top contenders

    were:

 

    - [[http://www.idsoftware.com][Quake II]]/[[http://www.bytonic.de/html/jake2.html][Jake2]] :: The Quake II engine was designed by ID software

         in 1997. All the source code was released by ID software into

         the Public Domain several years ago, and as a result it has

         been ported to many different languages. This engine was

         famous for its advanced use of realistic shading and it had

         decent and fast physics simulation. The main advantage of the

         Quake II engine is its simplicity, but I ultimately rejected

         it because the engine is too tied to the concept of a

         first-person shooter game. One of the problems I had was that

         there does not seem to be any easy way to attach multiple

         cameras to a single character. There are also several physics

         clipping issues that are corrected in a way that only applies

         to the main character and do not apply to arbitrary objects.

 

    - [[http://source.valvesoftware.com/][Source Engine]]     :: The Source Engine evolved from the Quake II

         and Quake I engines and is used by Valve in the Half-Life

         series of games. The physics simulation in the Source Engine

         is quite accurate and probably the best out of all the engines

         I investigated. There is also an extensive community actively

         working with the engine. However, applications that use the

         Source Engine must be written in C++, the code is not open, it

         only runs on Windows, and the tools that come with the SDK to

         handle models and textures are complicated and awkward to use.

 

    -  [[http://jmonkeyengine.com/][jMonkeyEngine3]] :: jMonkeyEngine3 is a new library for creating

         games in Java. It uses OpenGL to render to the screen and uses

         screengraphs to avoid drawing things that do not appear on the

         screen. It has an active community and several games in the

         pipeline. The engine was not built to serve any particular

         game but is instead meant to be used for any 3D game. 

 

    I chose jMonkeyEngine3 because it had the most features out of all

    the free projects I looked at, and because I could then write my

    code in clojure, an implementation of =LISP= that runs on the JVM.

 

 ** =CORTEX= uses Blender to create creature models

 

    For the simple worm-like creatures I will use later on in this

    thesis, I could define a simple API in =CORTEX= that would allow

    one to create boxes, spheres, etc., and leave that API as the sole

    way to create creatures. However, for =CORTEX= to truly be useful

    for other projects, it needs a way to construct complicated

    creatures. If possible, it would be nice to leverage work that has

    already been done by the community of 3D modelers, or at least

    enable people who are talented at modeling but not programming to

    design =CORTEX= creatures.

 

    Therefore I use Blender, a free 3D modeling program, as the main

    way to create creatures in =CORTEX=. However, the creatures modeled

    in Blender must also be simple to simulate in jMonkeyEngine3's game

    engine, and must also be easy to rig with =CORTEX='s senses. I

    accomplish this with extensive use of Blender's ``empty nodes.''

 

    Empty nodes have no mass, physical presence, or appearance, but

    they can hold metadata and have names. I use a tree structure of

    empty nodes to specify senses in the following manner:

 

    - Create a single top-level empty node whose name is the name of

      the sense.

    - Add empty nodes which each contain meta-data relevant to the

      sense, including a UV-map describing the number/distribution of

      sensors if applicable.

    - Make each empty-node the child of the top-level node.

      

    #+caption: An example of annotating a creature model with empty

    #+caption: nodes to describe the layout of senses. There are 

    #+caption: multiple empty nodes which each describe the position

    #+caption: of muscles, ears, eyes, or joints.

    #+name: sense-nodes

    #+ATTR_LaTeX: :width 10cm

    [[./images/empty-sense-nodes.png]]

 

 ** Bodies are composed of segments connected by joints

 

    Blender is a general purpose animation tool, which has been used in

    the past to create high quality movies such as Sintel

    (\cite{blender}). Though Blender can model and render even

    complicated things like water, it is crucial to keep models that

    are meant to be simulated as creatures simple. =Bullet=, which

    =CORTEX= uses though jMonkeyEngine3, is a rigid-body physics

    system. This offers a compromise between the expressiveness of a

    game level and the speed at which it can be simulated, and it means

    that creatures should be naturally expressed as rigid components

    held together by joint constraints.

 

    But humans are more like a squishy bag wrapped around some hard

    bones which define the overall shape. When we move, our skin bends

    and stretches to accommodate the new positions of our bones.

 

    One way to make bodies composed of rigid pieces connected by joints

    /seem/ more human-like is to use an /armature/, (or /rigging/)

    system, which defines a overall ``body mesh'' and defines how the

    mesh deforms as a function of the position of each ``bone'' which

    is a standard rigid body. This technique is used extensively to

    model humans and create realistic animations. It is not a good

    technique for physical simulation because it is a lie -- the skin

    is not a physical part of the simulation and does not interact with

    any objects in the world or itself. Objects will pass right though

    the skin until they come in contact with the underlying bone, which

    is a physical object. Without simulating the skin, the sense of

    touch has little meaning, and the creature's own vision will lie to

    it about the true extent of its body. Simulating the skin as a

    physical object requires some way to continuously update the

    physical model of the skin along with the movement of the bones,

    which is unacceptably slow compared to rigid body simulation.

 

    Therefore, instead of using the human-like ``bony meatbag''

    approach, I decided to base my body plans on multiple solid objects

    that are connected by joints, inspired by the robot =EVE= from the

    movie WALL-E.

    

    #+caption: =EVE= from the movie WALL-E.  This body plan turns 

    #+caption: out to be much better suited to my purposes than a more 

    #+caption: human-like one.

    #+ATTR_LaTeX: :width 10cm

    [[./images/Eve.jpg]]

 

    =EVE='s body is composed of several rigid components that are held

    together by invisible joint constraints. This is what I mean by

    /eve-like/. The main reason that I use eve-like bodies is for

    simulation efficiency, and so that there will be correspondence

    between the AI's senses and the physical presence of its body. Each

    individual section is simulated by a separate rigid body that

    corresponds exactly with its visual representation and does not

    change. Sections are connected by invisible joints that are well

    supported in jMonkeyEngine3. Bullet, the physics backend for

    jMonkeyEngine3, can efficiently simulate hundreds of rigid bodies

    connected by joints. Just because sections are rigid does not mean

    they have to stay as one piece forever; they can be dynamically

    replaced with multiple sections to simulate splitting in two. This

    could be used to simulate retractable claws or =EVE='s hands, which

    are able to coalesce into one object in the movie.

 

 *** Solidifying/Connecting a body

 

     =CORTEX= creates a creature in two steps: first, it traverses the

     nodes in the Blender file and creates physical representations for

     any of them that have mass defined in their Blender meta-data.

 

    #+caption: Program for iterating through the nodes in a blender file

    #+caption: and generating physical jMonkeyEngine3 objects with mass

    #+caption: and a matching physics shape.

    #+name: physical

    #+begin_listing clojure

    #+begin_src clojure

 (defn physical!

   "Iterate through the nodes in creature and make them real physical

    objects in the simulation."

   [#^Node creature]

   (dorun

    (map

     (fn [geom]

       (let [physics-control

             (RigidBodyControl.

              (HullCollisionShape.

               (.getMesh geom))

              (if-let [mass (meta-data geom "mass")]

                (float mass) (float 1)))]

         (.addControl geom physics-control)))

     (filter #(isa? (class %) Geometry )

             (node-seq creature)))))

    #+end_src

    #+end_listing

    

     The next step to making a proper body is to connect those pieces

     together with joints. jMonkeyEngine has a large array of joints

     available via =bullet=, such as Point2Point, Cone, Hinge, and a

     generic Six Degree of Freedom joint, with or without spring

     restitution. 

 

     Joints are treated a lot like proper senses, in that there is a

     top-level empty node named ``joints'' whose children each

     represent a joint.

 

     #+caption: View of the hand model in Blender showing the main ``joints''

     #+caption: node (highlighted in yellow) and its children which each

     #+caption: represent a joint in the hand. Each joint node has metadata

     #+caption: specifying what sort of joint it is.

     #+name: blender-hand

     #+ATTR_LaTeX: :width 10cm

     [[./images/hand-screenshot1.png]]

 

 

     =CORTEX='s procedure for binding the creature together with joints

     is as follows:

     

     - Find the children of the ``joints'' node.

     - Determine the two spatials the joint is meant to connect.

     - Create the joint based on the meta-data of the empty node.

 

     The higher order function =sense-nodes= from =cortex.sense=

     simplifies finding the joints based on their parent ``joints''

     node.

 

    #+caption: Retrieving the children empty nodes from a single 

    #+caption: named empty node is a common pattern in =CORTEX=.

    #+caption: Further instances of this technique for the senses 

    #+caption: will be omitted

    #+name: get-empty-nodes

    #+begin_listing clojure

    #+begin_src clojure

 (defn sense-nodes

   "For some senses there is a special empty Blender node whose

    children are considered markers for an instance of that sense. This

    function generates functions to find those children, given the name

    of the special parent node."

   [parent-name]

   (fn [#^Node creature]

     (if-let [sense-node (.getChild creature parent-name)]

       (seq (.getChildren sense-node)) [])))

 

 (def

   ^{:doc "Return the children of the creature's \"joints\" node."

     :arglists '([creature])}

   joints

   (sense-nodes "joints"))

    #+end_src

    #+end_listing

 

     To find a joint's targets, =CORTEX= creates a small cube, centered

     around the empty-node, and grows the cube exponentially until it

     intersects two physical objects. The objects are ordered according

     to the joint's rotation, with the first one being the object that

     has more negative coordinates in the joint's reference frame.

     Because the objects must be physical, the empty-node itself

     escapes detection. Because the objects must be physical,

     =joint-targets= must be called /after/ =physical!= is called.

    

     #+caption: Program to find the targets of a joint node by 

     #+caption: exponentially growth of a search cube.

     #+name: joint-targets

     #+begin_listing clojure

     #+begin_src clojure

 (defn joint-targets

   "Return the two closest two objects to the joint object, ordered

   from bottom to top according to the joint's rotation."

   [#^Node parts #^Node joint]

   (loop [radius (float 0.01)]

     (let [results (CollisionResults.)]

       (.collideWith

        parts

        (BoundingBox. (.getWorldTranslation joint)

                      radius radius radius) results)

       (let [targets

             (distinct

              (map  #(.getGeometry %) results))]

         (if (>= (count targets) 2)

           (sort-by

            #(let [joint-ref-frame-position

                   (jme-to-blender

                    (.mult

                     (.inverse (.getWorldRotation joint))

                     (.subtract (.getWorldTranslation %)

                                (.getWorldTranslation joint))))]

               (.dot (Vector3f. 1 1 1) joint-ref-frame-position))                  

            (take 2 targets))

           (recur (float (* radius 2))))))))

     #+end_src

     #+end_listing

    

     Once =CORTEX= finds all joints and targets, it creates them using

     a dispatch on the metadata of each joint node.

 

     #+caption: Program to dispatch on Blender metadata and create joints

     #+caption: suitable for physical simulation.

     #+name: joint-dispatch

     #+begin_listing clojure

     #+begin_src clojure

 (defmulti joint-dispatch

   "Translate Blender pseudo-joints into real JME joints."

   (fn [constraints & _] 

     (:type constraints)))

 

 (defmethod joint-dispatch :point

   [constraints control-a control-b pivot-a pivot-b rotation]

   (doto (SixDofJoint. control-a control-b pivot-a pivot-b false)

     (.setLinearLowerLimit Vector3f/ZERO)

     (.setLinearUpperLimit Vector3f/ZERO)))

 

 (defmethod joint-dispatch :hinge

   [constraints control-a control-b pivot-a pivot-b rotation]

   (let [axis (if-let [axis (:axis constraints)] axis Vector3f/UNIT_X)

         [limit-1 limit-2] (:limit constraints)

         hinge-axis (.mult rotation (blender-to-jme axis))]

     (doto (HingeJoint. control-a control-b pivot-a pivot-b 

                        hinge-axis hinge-axis)

       (.setLimit limit-1 limit-2))))

 

 (defmethod joint-dispatch :cone

   [constraints control-a control-b pivot-a pivot-b rotation]

   (let [limit-xz (:limit-xz constraints)

         limit-xy (:limit-xy constraints)

         twist    (:twist constraints)]

     (doto (ConeJoint. control-a control-b pivot-a pivot-b

                       rotation rotation)

       (.setLimit (float limit-xz) (float limit-xy)

                  (float twist)))))

     #+end_src

     #+end_listing

 

     All that is left for joints is to combine the above pieces into

     something that can operate on the collection of nodes that a

     Blender file represents.

 

     #+caption: Program to completely create a joint given information 

     #+caption: from a Blender file.

     #+name: connect

     #+begin_listing clojure

    #+begin_src clojure

 (defn connect

   "Create a joint between 'obj-a and 'obj-b at the location of

   'joint. The type of joint is determined by the metadata on 'joint.

 

    Here are some examples:

    {:type :point}

    {:type :hinge  :limit [0 (/ Math/PI 2)] :axis (Vector3f. 0 1 0)}

    (:axis defaults to (Vector3f. 1 0 0) if not provided for hinge joints)

 

    {:type :cone :limit-xz 0]

                 :limit-xy 0]

                 :twist 0]}   (use XZY rotation mode in Blender!)"

   [#^Node obj-a #^Node obj-b #^Node joint]

   (let [control-a (.getControl obj-a RigidBodyControl)

         control-b (.getControl obj-b RigidBodyControl)

         joint-center (.getWorldTranslation joint)

         joint-rotation (.toRotationMatrix (.getWorldRotation joint))

         pivot-a (world-to-local obj-a joint-center)

         pivot-b (world-to-local obj-b joint-center)]

     (if-let

         [constraints (map-vals eval (read-string (meta-data joint "joint")))]

       ;; A side-effect of creating a joint registers

       ;; it with both physics objects which in turn

       ;; will register the joint with the physics system

       ;; when the simulation is started.

         (joint-dispatch constraints

                         control-a control-b

                         pivot-a pivot-b

                         joint-rotation))))

     #+end_src

     #+end_listing

 

     In general, whenever =CORTEX= exposes a sense (or in this case

     physicality), it provides a function of the type =sense!=, which

     takes in a collection of nodes and augments it to support that

     sense. The function returns any controls necessary to use that

     sense. In this case =body!= creates a physical body and returns no

     control functions.

 

     #+caption: Program to give joints to a creature.

     #+name: joints

     #+begin_listing clojure

     #+begin_src clojure

 (defn joints!

   "Connect the solid parts of the creature with physical joints. The

    joints are taken from the \"joints\" node in the creature."

   [#^Node creature]

   (dorun

    (map

     (fn [joint]

       (let [[obj-a obj-b] (joint-targets creature joint)]

         (connect obj-a obj-b joint)))

     (joints creature))))

 (defn body!

   "Endow the creature with a physical body connected with joints.  The

    particulars of the joints and the masses of each body part are

    determined in Blender."

   [#^Node creature]

   (physical! creature)

   (joints! creature))

     #+end_src

     #+end_listing

 

     All of the code you have just seen amounts to only 130 lines, yet

     because it builds on top of Blender and jMonkeyEngine3, those few

     lines pack quite a punch!

 

     The hand from figure \ref{blender-hand}, which was modeled after

     my own right hand, can now be given joints and simulated as a

     creature.

    

     #+caption: With the ability to create physical creatures from Blender,

     #+caption: =CORTEX= gets one step closer to becoming a full creature

     #+caption: simulation environment.

     #+name: physical-hand

     #+ATTR_LaTeX: :width 15cm

     [[./images/physical-hand.png]]

 

 ** Sight reuses standard video game components...

 

    Vision is one of the most important senses for humans, so I need to

    build a simulated sense of vision for my AI. I will do this with

    simulated eyes. Each eye can be independently moved and should see

    its own version of the world depending on where it is.

 

    Making these simulated eyes a reality is simple because

    jMonkeyEngine already contains extensive support for multiple views

    of the same 3D simulated world. The reason jMonkeyEngine has this

    support is because the support is necessary to create games with

    split-screen views. Multiple views are also used to create

    efficient pseudo-reflections by rendering the scene from a certain

    perspective and then projecting it back onto a surface in the 3D

    world.

 

    #+caption: jMonkeyEngine supports multiple views to enable 

    #+caption: split-screen games, like GoldenEye, which was one of 

    #+caption: the first games to use split-screen views.

    #+name: goldeneye

    #+ATTR_LaTeX: :width 10cm

    [[./images/goldeneye-4-player.png]]

 

 *** A Brief Description of jMonkeyEngine's Rendering Pipeline

 

     jMonkeyEngine allows you to create a =ViewPort=, which represents a

     view of the simulated world. You can create as many of these as you

     want. Every frame, the =RenderManager= iterates through each

     =ViewPort=, rendering the scene in the GPU. For each =ViewPort= there

     is a =FrameBuffer= which represents the rendered image in the GPU.

   

     #+caption: =ViewPorts= are cameras in the world. During each frame, 

     #+caption: the =RenderManager= records a snapshot of what each view 

     #+caption: is currently seeing; these snapshots are =FrameBuffer= objects.

     #+name: rendermanagers

     #+ATTR_LaTeX: :width 10cm

     [[./images/diagram_rendermanager2.png]]

 

     Each =ViewPort= can have any number of attached =SceneProcessor=

     objects, which are called every time a new frame is rendered. A

     =SceneProcessor= receives its =ViewPort's= =FrameBuffer= and can do

     whatever it wants to the data.  Often this consists of invoking GPU

     specific operations on the rendered image.  The =SceneProcessor= can

     also copy the GPU image data to RAM and process it with the CPU.

 

 *** Appropriating Views for Vision

 

     Each eye in the simulated creature needs its own =ViewPort= so

     that it can see the world from its own perspective. To this

     =ViewPort=, I add a =SceneProcessor= that feeds the visual data to

     any arbitrary continuation function for further processing. That

     continuation function may perform both CPU and GPU operations on

     the data. To make this easy for the continuation function, the

     =SceneProcessor= maintains appropriately sized buffers in RAM to

     hold the data. It does not do any copying from the GPU to the CPU

     itself because it is a slow operation.

 

     #+caption: Function to make the rendered scene in jMonkeyEngine 

     #+caption: available for further processing.

     #+name: pipeline-1 

     #+begin_listing clojure

     #+begin_src clojure

 (defn vision-pipeline

   "Create a SceneProcessor object which wraps a vision processing

   continuation function. The continuation is a function that takes 

   [#^Renderer r #^FrameBuffer fb #^ByteBuffer b #^BufferedImage bi],

   each of which has already been appropriately sized."

   [continuation]

   (let [byte-buffer (atom nil)

 	renderer (atom nil)

         image (atom nil)]

   (proxy [SceneProcessor] []

     (initialize

      [renderManager viewPort]

      (let [cam (.getCamera viewPort)

 	   width (.getWidth cam)

 	   height (.getHeight cam)]

        (reset! renderer (.getRenderer renderManager))

        (reset! byte-buffer

 	     (BufferUtils/createByteBuffer

 	      (* width height 4)))

         (reset! image (BufferedImage.

                       width height

                       BufferedImage/TYPE_4BYTE_ABGR))))

     (isInitialized [] (not (nil? @byte-buffer)))

     (reshape [_ _ _])

     (preFrame [_])

     (postQueue [_])

     (postFrame

      [#^FrameBuffer fb]

      (.clear @byte-buffer)

      (continuation @renderer fb @byte-buffer @image))

     (cleanup []))))

     #+end_src

     #+end_listing

 

     The continuation function given to =vision-pipeline= above will be

     given a =Renderer= and three containers for image data. The

     =FrameBuffer= references the GPU image data, but the pixel data

     can not be used directly on the CPU. The =ByteBuffer= and

     =BufferedImage= are initially "empty" but are sized to hold the

     data in the =FrameBuffer=. I call transferring the GPU image data

     to the CPU structures "mixing" the image data.

 

 *** Optical sensor arrays are described with images and referenced with metadata

 

     The vision pipeline described above handles the flow of rendered

     images. Now, =CORTEX= needs simulated eyes to serve as the source

     of these images.

 

     An eye is described in Blender in the same way as a joint. They

     are zero dimensional empty objects with no geometry whose local

     coordinate system determines the orientation of the resulting eye.

     All eyes are children of a parent node named "eyes" just as all

     joints have a parent named "joints". An eye binds to the nearest

     physical object with =bind-sense=.

 

     #+caption: Here, the camera is created based on metadata on the

     #+caption: eye-node and attached to the nearest physical object 

     #+caption: with =bind-sense=

     #+name: add-eye

     #+begin_listing clojure

     #+begin_src clojure

 (defn add-eye!

   "Create a Camera centered on the current position of 'eye which

    follows the closest physical node in 'creature. The camera will

    point in the X direction and use the Z vector as up as determined

    by the rotation of these vectors in Blender coordinate space. Use

    XZY rotation for the node in blender."

   [#^Node creature #^Spatial eye]

   (let [target (closest-node creature eye)

         [cam-width cam-height] 

         ;;[640 480] ;; graphics card on laptop doesn't support

                     ;; arbitrary dimensions.

         (eye-dimensions eye)

         cam (Camera. cam-width cam-height)

         rot (.getWorldRotation eye)]

     (.setLocation cam (.getWorldTranslation eye))

     (.lookAtDirection

      cam                           ; this part is not a mistake and

      (.mult rot Vector3f/UNIT_X)   ; is consistent with using Z in

      (.mult rot Vector3f/UNIT_Y))  ; Blender as the UP vector.

     (.setFrustumPerspective

      cam (float 45)

      (float (/ (.getWidth cam) (.getHeight cam)))

      (float 1)

      (float 1000))

     (bind-sense target cam) cam))

     #+end_src

     #+end_listing

 

 *** Simulated Retina 

 

     An eye is a surface (the retina) which contains many discrete

     sensors to detect light. These sensors can have different

     light-sensing properties. In humans, each discrete sensor is

     sensitive to red, blue, green, or gray. These different types of

     sensors can have different spatial distributions along the retina.

     In humans, there is a fovea in the center of the retina which has

     a very high density of color sensors, and a blind spot which has

     no sensors at all. Sensor density decreases in proportion to

     distance from the fovea.

 

     I want to be able to model any retinal configuration, so my

     eye-nodes in Blender contain metadata pointing to images that

     describe the precise position of the individual sensors using

     white pixels. The meta-data also describes the precise sensitivity

     to light that the sensors described in the image have. An eye can

     contain any number of these images. For example, the metadata for

     an eye might look like this:

 

     #+begin_src clojure

 {0xFF0000 "Models/test-creature/retina-small.png"}

     #+end_src

 

     #+caption: An example retinal profile image. White pixels are 

     #+caption: photo-sensitive elements. The distribution of white 

     #+caption: pixels is denser in the middle and falls off at the 

     #+caption: edges and is inspired by the human retina.

     #+name: retina

     #+ATTR_LaTeX: :width 7cm

     [[./images/retina-small.png]]

 

     Together, the number 0xFF0000 and the image above describe the

     placement of red-sensitive sensory elements.

 

     Meta-data to very crudely approximate a human eye might be

     something like this:

 

     #+begin_src clojure

 (let [retinal-profile "Models/test-creature/retina-small.png"]

   {0xFF0000 retinal-profile

    0x00FF00 retinal-profile

    0x0000FF retinal-profile

    0xFFFFFF retinal-profile})

     #+end_src

 

     The numbers that serve as keys in the map determine a sensor's

     relative sensitivity to the channels red, green, and blue. These

     sensitivity values are packed into an integer in the order

     =|_|R|G|B|= in 8-bit fields. The RGB values of a pixel in the

     image are added together with these sensitivities as linear

     weights. Therefore, 0xFF0000 means sensitive to red only while

     0xFFFFFF means sensitive to all colors equally (gray).

 

     #+caption: This is the core of vision in =CORTEX=. A given eye node 

     #+caption: is converted into a function that returns visual

     #+caption: information from the simulation.

     #+name: vision-kernel

     #+begin_listing clojure

     #+BEGIN_SRC clojure

 (defn vision-kernel

   "Returns a list of functions, each of which will return a color

    channel's worth of visual information when called inside a running

    simulation."

   [#^Node creature #^Spatial eye & {skip :skip :or {skip 0}}]

   (let [retinal-map (retina-sensor-profile eye)

         camera (add-eye! creature eye)

         vision-image

         (atom

          (BufferedImage. (.getWidth camera)

                          (.getHeight camera)

                          BufferedImage/TYPE_BYTE_BINARY))

         register-eye!

         (runonce

          (fn [world]

            (add-camera!

             world camera

             (let [counter  (atom 0)]

               (fn [r fb bb bi]

                 (if (zero? (rem (swap! counter inc) (inc skip)))

                   (reset! vision-image

                           (BufferedImage! r fb bb bi))))))))]

      (vec

       (map

        (fn [[key image]]

          (let [whites (white-coordinates image)

                topology (vec (collapse whites))

                sensitivity (sensitivity-presets key key)]

            (attached-viewport.

             (fn [world]

               (register-eye! world)

               (vector

                topology

                (vec 

                 (for [[x y] whites]

                   (pixel-sense 

                    sensitivity

                    (.getRGB @vision-image x y))))))

             register-eye!)))

          retinal-map))))

     #+END_SRC

     #+end_listing

 

     Note that because each of the functions generated by

     =vision-kernel= shares the same =register-eye!= function, the eye

     will be registered only once the first time any of the functions

     from the list returned by =vision-kernel= is called. Each of the

     functions returned by =vision-kernel= also allows access to the

     =Viewport= through which it receives images.

 

     All the hard work has been done; all that remains is to apply

     =vision-kernel= to each eye in the creature and gather the results

     into one list of functions.

 

 

     #+caption: With =vision!=, =CORTEX= is already a fine simulation 

     #+caption: environment for experimenting with different types of 

     #+caption: eyes.

     #+name: vision!

     #+begin_listing clojure

     #+BEGIN_SRC clojure

 (defn vision!

   "Returns a list of functions, each of which returns visual sensory

    data when called inside a running simulation."

   [#^Node creature & {skip :skip :or {skip 0}}]

   (reduce

    concat 

    (for [eye (eyes creature)]

      (vision-kernel creature eye))))

     #+END_SRC

     #+end_listing

 

     #+caption: Simulated vision with a test creature and the 

     #+caption: human-like eye approximation. Notice how each channel

     #+caption: of the eye responds differently to the differently 

     #+caption: colored balls.

     #+name: worm-vision-test.

     #+ATTR_LaTeX: :width 13cm

     [[./images/worm-vision.png]]

 

     The vision code is not much more complicated than the body code,

     and enables multiple further paths for simulated vision. For

     example, it is quite easy to create bifocal vision -- you just

     make two eyes next to each other in blender! It is also possible

     to encode vision transforms in the retinal files. For example, the

     human like retina file in figure \ref{retina} approximates a

     log-polar transform.

 

     This vision code has already been absorbed by the jMonkeyEngine

     community and is now (in modified form) part of a system for

     capturing in-game video to a file.

 

 ** ...but hearing must be built from scratch

 

    At the end of this chapter I will have simulated ears that work the

    same way as the simulated eyes in the last chapter. I will be able to

    place any number of ear-nodes in a Blender file, and they will bind to

    the closest physical object and follow it as it moves around. Each ear

    will provide access to the sound data it picks up between every frame.

 

    Hearing is one of the more difficult senses to simulate, because there

    is less support for obtaining the actual sound data that is processed

    by jMonkeyEngine3. There is no "split-screen" support for rendering

    sound from different points of view, and there is no way to directly

    access the rendered sound data.

 

    =CORTEX='s hearing is unique because it does not have any

    limitations compared to other simulation environments. As far as I

    know, there is no other system that supports multiple listeners,

    and the sound demo at the end of this chapter is the first time

    it's been done in a video game environment.

 

 *** Brief Description of jMonkeyEngine's Sound System

 

    jMonkeyEngine's sound system works as follows:

 

    - jMonkeyEngine uses the =AppSettings= for the particular

      application to determine what sort of =AudioRenderer= should be

      used.

    - Although some support is provided for multiple AudioRenderer

      backends, jMonkeyEngine at the time of this writing will either

      pick no =AudioRenderer= at all, or the =LwjglAudioRenderer=.

    - jMonkeyEngine tries to figure out what sort of system you're

      running and extracts the appropriate native libraries.

    - The =LwjglAudioRenderer= uses the [[http://lwjgl.org/][=LWJGL=]] (LightWeight Java Game

      Library) bindings to interface with a C library called [[http://kcat.strangesoft.net/openal.html][=OpenAL=]]

    - =OpenAL= renders the 3D sound and feeds the rendered sound

      directly to any of various sound output devices with which it

      knows how to communicate.

   

    A consequence of this is that there's no way to access the actual

    sound data produced by =OpenAL=. Even worse, =OpenAL= only supports

    one /listener/ (it renders sound data from only one perspective),

    which normally isn't a problem for games, but becomes a problem

    when trying to make multiple AI creatures that can each hear the

    world from a different perspective.

 

    To make many AI creatures in jMonkeyEngine that can each hear the

    world from their own perspective, or to make a single creature with

    many ears, it is necessary to go all the way back to =OpenAL= and

    implement support for simulated hearing there.

 

 *** Extending =OpenAl=

 

     Extending =OpenAL= to support multiple listeners requires 500

     lines of =C= code and is too complicated to mention here. Instead,

     I will show a small amount of extension code and go over the high

     level strategy. Full source is of course available with the

     =CORTEX= distribution if you're interested.

 

     =OpenAL= goes to great lengths to support many different systems,

     all with different sound capabilities and interfaces. It

     accomplishes this difficult task by providing code for many

     different sound backends in pseudo-objects called /Devices/.

     There's a device for the Linux Open Sound System and the Advanced

     Linux Sound Architecture, there's one for Direct Sound on Windows,

     and there's even one for Solaris. =OpenAL= solves the problem of

     platform independence by providing all these Devices.

 

     Wrapper libraries such as LWJGL are free to examine the system on

     which they are running and then select an appropriate device for

     that system.

 

     There are also a few "special" devices that don't interface with

     any particular system. These include the Null Device, which

     doesn't do anything, and the Wave Device, which writes whatever

     sound it receives to a file, if everything has been set up

     correctly when configuring =OpenAL=.

 

     Actual mixing (Doppler shift and distance.environment-based

     attenuation) of the sound data happens in the Devices, and they

     are the only point in the sound rendering process where this data

     is available.

 

     Therefore, in order to support multiple listeners, and get the

     sound data in a form that the AIs can use, it is necessary to

     create a new Device which supports this feature.

 

     Adding a device to OpenAL is rather tricky -- there are five

     separate files in the =OpenAL= source tree that must be modified

     to do so. I named my device the "Multiple Audio Send" Device, or

     =Send= Device for short, since it sends audio data back to the

     calling application like an Aux-Send cable on a mixing board.

 

     The main idea behind the Send device is to take advantage of the

     fact that LWJGL only manages one /context/ when using OpenAL. A

     /context/ is like a container that holds samples and keeps track

     of where the listener is. In order to support multiple listeners,

     the Send device identifies the LWJGL context as the master

     context, and creates any number of slave contexts to represent

     additional listeners. Every time the device renders sound, it

     synchronizes every source from the master LWJGL context to the

     slave contexts. Then, it renders each context separately, using a

     different listener for each one. The rendered sound is made

     available via JNI to jMonkeyEngine.

 

     Switching between contexts is not the normal operation of a

     Device, and one of the problems with doing so is that a Device

     normally keeps around a few pieces of state such as the

     =ClickRemoval= array above which will become corrupted if the

     contexts are not rendered in parallel. The solution is to create a

     copy of this normally global device state for each context, and

     copy it back and forth into and out of the actual device state

     whenever a context is rendered.

 

     The core of the =Send= device is the =syncSources= function, which

     does the job of copying all relevant data from one context to

     another. 

 

     #+caption: Program for extending =OpenAL= to support multiple

     #+caption: listeners via context copying/switching.

     #+name: sync-openal-sources

     #+begin_listing c

     #+BEGIN_SRC c

 void syncSources(ALsource *masterSource, ALsource *slaveSource, 

 		 ALCcontext *masterCtx, ALCcontext *slaveCtx){

   ALuint master = masterSource->source;

   ALuint slave = slaveSource->source;

   ALCcontext *current = alcGetCurrentContext();

 

   syncSourcef(master,slave,masterCtx,slaveCtx,AL_PITCH);

   syncSourcef(master,slave,masterCtx,slaveCtx,AL_GAIN);

   syncSourcef(master,slave,masterCtx,slaveCtx,AL_MAX_DISTANCE);

   syncSourcef(master,slave,masterCtx,slaveCtx,AL_ROLLOFF_FACTOR);

   syncSourcef(master,slave,masterCtx,slaveCtx,AL_REFERENCE_DISTANCE);

   syncSourcef(master,slave,masterCtx,slaveCtx,AL_MIN_GAIN);

   syncSourcef(master,slave,masterCtx,slaveCtx,AL_MAX_GAIN);

   syncSourcef(master,slave,masterCtx,slaveCtx,AL_CONE_OUTER_GAIN);

   syncSourcef(master,slave,masterCtx,slaveCtx,AL_CONE_INNER_ANGLE);

   syncSourcef(master,slave,masterCtx,slaveCtx,AL_CONE_OUTER_ANGLE);

   syncSourcef(master,slave,masterCtx,slaveCtx,AL_SEC_OFFSET);

   syncSourcef(master,slave,masterCtx,slaveCtx,AL_SAMPLE_OFFSET);

   syncSourcef(master,slave,masterCtx,slaveCtx,AL_BYTE_OFFSET);

     

   syncSource3f(master,slave,masterCtx,slaveCtx,AL_POSITION);

   syncSource3f(master,slave,masterCtx,slaveCtx,AL_VELOCITY);

   syncSource3f(master,slave,masterCtx,slaveCtx,AL_DIRECTION);

   

   syncSourcei(master,slave,masterCtx,slaveCtx,AL_SOURCE_RELATIVE);

   syncSourcei(master,slave,masterCtx,slaveCtx,AL_LOOPING);

 

   alcMakeContextCurrent(masterCtx);

   ALint source_type;

   alGetSourcei(master, AL_SOURCE_TYPE, &source_type);

 

   // Only static sources are currently synchronized! 

   if (AL_STATIC == source_type){

     ALint master_buffer;

     ALint slave_buffer;

     alGetSourcei(master, AL_BUFFER, &master_buffer);

     alcMakeContextCurrent(slaveCtx);

     alGetSourcei(slave, AL_BUFFER, &slave_buffer);

     if (master_buffer != slave_buffer){

       alSourcei(slave, AL_BUFFER, master_buffer);

     }

   }

   

   // Synchronize the state of the two sources.

   alcMakeContextCurrent(masterCtx);

   ALint masterState;

   ALint slaveState;

 

   alGetSourcei(master, AL_SOURCE_STATE, &masterState);

   alcMakeContextCurrent(slaveCtx);

   alGetSourcei(slave, AL_SOURCE_STATE, &slaveState);

 

   if (masterState != slaveState){

     switch (masterState){

     case AL_INITIAL : alSourceRewind(slave); break;

     case AL_PLAYING : alSourcePlay(slave);   break;

     case AL_PAUSED  : alSourcePause(slave);  break;

     case AL_STOPPED : alSourceStop(slave);   break;

     }

   }

   // Restore whatever context was previously active.

   alcMakeContextCurrent(current);

 }

     #+END_SRC

     #+end_listing

 

     With this special context-switching device, and some ugly JNI

     bindings that are not worth mentioning, =CORTEX= gains the ability

     to access multiple sound streams from =OpenAL=. 

 

     #+caption: Program to create an ear from a blender empty node. The ear

     #+caption: follows around the nearest physical object and passes 

     #+caption: all sensory data to a continuation function.

     #+name: add-ear

     #+begin_listing clojure

     #+BEGIN_SRC clojure

 (defn add-ear!  

   "Create a Listener centered on the current position of 'ear 

    which follows the closest physical node in 'creature and 

    sends sound data to 'continuation."

   [#^Application world #^Node creature #^Spatial ear continuation]

   (let [target (closest-node creature ear)

         lis (Listener.)

         audio-renderer (.getAudioRenderer world)

         sp (hearing-pipeline continuation)]

     (.setLocation lis (.getWorldTranslation ear))

     (.setRotation lis (.getWorldRotation ear))

     (bind-sense target lis)

     (update-listener-velocity! target lis)

     (.addListener audio-renderer lis)

     (.registerSoundProcessor audio-renderer lis sp)))

     #+END_SRC

     #+end_listing

     

     The =Send= device, unlike most of the other devices in =OpenAL=,

     does not render sound unless asked. This enables the system to

     slow down or speed up depending on the needs of the AIs who are

     using it to listen. If the device tried to render samples in

     real-time, a complicated AI whose mind takes 100 seconds of

     computer time to simulate 1 second of AI-time would miss almost

     all of the sound in its environment!

 

     #+caption: Program to enable arbitrary hearing in =CORTEX=

     #+name: hearing

     #+begin_listing clojure

 #+BEGIN_SRC clojure

 (defn hearing-kernel

   "Returns a function which returns auditory sensory data when called

    inside a running simulation."

   [#^Node creature #^Spatial ear]

   (let [hearing-data (atom [])

         register-listener!

         (runonce 

          (fn [#^Application world]

            (add-ear!

             world creature ear

             (comp #(reset! hearing-data %)

                   byteBuffer->pulse-vector))))]

     (fn [#^Application world]

       (register-listener! world)

       (let [data @hearing-data

             topology              

             (vec (map #(vector % 0) (range 0 (count data))))]

         [topology data]))))

     

 (defn hearing!

   "Endow the creature in a particular world with the sense of

    hearing. Will return a sequence of functions, one for each ear,

    which when called will return the auditory data from that ear."

   [#^Node creature]

   (for [ear (ears creature)]

     (hearing-kernel creature ear)))

     #+END_SRC

     #+end_listing

 

     Armed with these functions, =CORTEX= is able to test possibly the

     first ever instance of multiple listeners in a video game engine

     based simulation!

 

     #+caption: Here a simple creature responds to sound by changing

     #+caption: its color from gray to green when the total volume

     #+caption: goes over a threshold.

     #+name: sound-test

     #+begin_listing java

     #+BEGIN_SRC java

 /**

  * Respond to sound!  This is the brain of an AI entity that 

  * hears its surroundings and reacts to them.

  */

 public void process(ByteBuffer audioSamples, 

 		    int numSamples, AudioFormat format) {

     audioSamples.clear();

     byte[] data = new byte[numSamples];

     float[] out = new float[numSamples];

     audioSamples.get(data);

     FloatSampleTools.

 	byte2floatInterleaved

 	(data, 0, out, 0, numSamples/format.getFrameSize(), format);

 

     float max = Float.NEGATIVE_INFINITY;

     for (float f : out){if (f > max) max = f;}

     audioSamples.clear();

 

     if (max > 0.1){

 	entity.getMaterial().setColor("Color", ColorRGBA.Green);

     }

     else {

 	entity.getMaterial().setColor("Color", ColorRGBA.Gray);

     }

     #+END_SRC

     #+end_listing

 

     #+caption: First ever simulation of multiple listeners in =CORTEX=.

     #+caption: Each cube is a creature which processes sound data with

     #+caption: the =process= function from listing \ref{sound-test}. 

     #+caption: the ball is constantly emitting a pure tone of

     #+caption: constant volume. As it approaches the cubes, they each

     #+caption: change color in response to the sound.

     #+name: sound-cubes.

     #+ATTR_LaTeX: :width 10cm

     [[./images/java-hearing-test.png]]

 

     This system of hearing has also been co-opted by the

     jMonkeyEngine3 community and is used to record audio for demo

     videos.

 

 ** Hundreds of hair-like elements provide a sense of touch

 

    Touch is critical to navigation and spatial reasoning and as such I

    need a simulated version of it to give to my AI creatures.

    

    Human skin has a wide array of touch sensors, each of which

    specialize in detecting different vibrational modes and pressures.

    These sensors can integrate a vast expanse of skin (i.e. your

    entire palm), or a tiny patch of skin at the tip of your finger.

    The hairs of the skin help detect objects before they even come

    into contact with the skin proper.

    

    However, touch in my simulated world can not exactly correspond to

    human touch because my creatures are made out of completely rigid

    segments that don't deform like human skin.

    

    Instead of measuring deformation or vibration, I surround each

    rigid part with a plenitude of hair-like objects (/feelers/) which

    do not interact with the physical world. Physical objects can pass

    through them with no effect. The feelers are able to tell when

    other objects pass through them, and they constantly report how

    much of their extent is covered. So even though the creature's body

    parts do not deform, the feelers create a margin around those body

    parts which achieves a sense of touch which is a hybrid between a

    human's sense of deformation and sense from hairs.

    

    Implementing touch in jMonkeyEngine follows a different technical

    route than vision and hearing. Those two senses piggybacked off

    jMonkeyEngine's 3D audio and video rendering subsystems. To

    simulate touch, I use jMonkeyEngine's physics system to execute

    many small collision detections, one for each feeler. The placement

    of the feelers is determined by a UV-mapped image which shows where

    each feeler should be on the 3D surface of the body.

 

 *** Defining Touch Meta-Data in Blender

 

     Each geometry can have a single UV map which describes the

     position of the feelers which will constitute its sense of touch.

     This image path is stored under the ``touch'' key. The image itself

     is black and white, with black meaning a feeler length of 0 (no

     feeler is present) and white meaning a feeler length of =scale=,

     which is a float stored under the key "scale".

 

     #+caption: Touch does not use empty nodes, to store metadata, 

     #+caption: because the metadata of each solid part of a 

     #+caption: creature's body is sufficient.

     #+name: touch-meta-data

     #+begin_listing clojure

     #+BEGIN_SRC  clojure

 (defn tactile-sensor-profile

   "Return the touch-sensor distribution image in BufferedImage format,

    or nil if it does not exist."

   [#^Geometry obj]

   (if-let [image-path (meta-data obj "touch")]

     (load-image image-path)))

 

 (defn tactile-scale

   "Return the length of each feeler. Default scale is 0.01

   jMonkeyEngine units."

   [#^Geometry obj]

   (if-let [scale (meta-data obj "scale")]

     scale 0.1))

     #+END_SRC

     #+end_listing

 

     Here is an example of a UV-map which specifies the position of

     touch sensors along the surface of the upper segment of a fingertip.

 

     #+caption: This is the tactile-sensor-profile for the upper segment 

     #+caption: of a fingertip. It defines regions of high touch sensitivity 

     #+caption: (where there are many white pixels) and regions of low 

     #+caption: sensitivity (where white pixels are sparse).

     #+name: fingertip-UV

     #+ATTR_LaTeX: :width 13cm

     [[./images/finger-UV.png]]

 

 *** Implementation Summary

   

     To simulate touch there are three conceptual steps. For each solid

     object in the creature, you first have to get UV image and scale

     parameter which define the position and length of the feelers.

     Then, you use the triangles which comprise the mesh and the UV

     data stored in the mesh to determine the world-space position and

     orientation of each feeler. Then once every frame, update these

     positions and orientations to match the current position and

     orientation of the object, and use physics collision detection to

     gather tactile data.

     

     Extracting the meta-data has already been described. The third

     step, physics collision detection, is handled in =touch-kernel=.

     Translating the positions and orientations of the feelers from the

     UV-map to world-space is itself a three-step process.

 

     - Find the triangles which make up the mesh in pixel-space and in

       world-space. \\(=triangles=, =pixel-triangles=).

 

     - Find the coordinates of each feeler in world-space. These are

       the origins of the feelers. (=feeler-origins=).

     

     - Calculate the normals of the triangles in world space, and add

       them to each of the origins of the feelers. These are the

       normalized coordinates of the tips of the feelers.

       (=feeler-tips=).

 

 *** Triangle Math

 

     The rigid objects which make up a creature have an underlying

     =Geometry=, which is a =Mesh= plus a =Material= and other

     important data involved with displaying the object.

     

     A =Mesh= is composed of =Triangles=, and each =Triangle= has three

     vertices which have coordinates in world space and UV space.

     

     Here, =triangles= gets all the world-space triangles which

     comprise a mesh, while =pixel-triangles= gets those same triangles

     expressed in pixel coordinates (which are UV coordinates scaled to

     fit the height and width of the UV image).

 

     #+caption: Programs to extract triangles from a geometry and get 

     #+caption: their vertices in both world and UV-coordinates.

     #+name: get-triangles

     #+begin_listing clojure

     #+BEGIN_SRC clojure

 (defn triangle

   "Get the triangle specified by triangle-index from the mesh."

   [#^Geometry geo triangle-index]

   (triangle-seq

    (let [scratch (Triangle.)]

      (.getTriangle (.getMesh geo) triangle-index scratch) scratch)))

 

 (defn triangles

   "Return a sequence of all the Triangles which comprise a given

    Geometry." 

   [#^Geometry geo]

   (map (partial triangle geo) (range (.getTriangleCount (.getMesh geo)))))

 

 (defn triangle-vertex-indices

   "Get the triangle vertex indices of a given triangle from a given

    mesh."

   [#^Mesh mesh triangle-index]

   (let [indices (int-array 3)]

     (.getTriangle mesh triangle-index indices)

     (vec indices)))

 

     (defn vertex-UV-coord

   "Get the UV-coordinates of the vertex named by vertex-index"

   [#^Mesh mesh vertex-index]

   (let [UV-buffer

         (.getData

          (.getBuffer

           mesh

           VertexBuffer$Type/TexCoord))]

     [(.get UV-buffer (* vertex-index 2))

      (.get UV-buffer (+ 1 (* vertex-index 2)))]))

 

 (defn pixel-triangle [#^Geometry geo image index]

   (let [mesh (.getMesh geo)

         width (.getWidth image)

         height (.getHeight image)]

     (vec (map (fn [[u v]] (vector (* width u) (* height v)))

               (map (partial vertex-UV-coord mesh)

                    (triangle-vertex-indices mesh index))))))

 

 (defn pixel-triangles 

   "The pixel-space triangles of the Geometry, in the same order as

    (triangles geo)"

   [#^Geometry geo image]

   (let [height (.getHeight image)

         width (.getWidth image)]

     (map (partial pixel-triangle geo image)

          (range (.getTriangleCount (.getMesh geo))))))

     #+END_SRC

     #+end_listing

     

 *** The Affine Transform from one Triangle to Another

 

     =pixel-triangles= gives us the mesh triangles expressed in pixel

     coordinates and =triangles= gives us the mesh triangles expressed

     in world coordinates. The tactile-sensor-profile gives the

     position of each feeler in pixel-space. In order to convert

     pixel-space coordinates into world-space coordinates we need

     something that takes coordinates on the surface of one triangle

     and gives the corresponding coordinates on the surface of another

     triangle.

     

     Triangles are [[http://mathworld.wolfram.com/AffineTransformation.html ][affine]], which means any triangle can be transformed

     into any other by a combination of translation, scaling, and

     rotation. The affine transformation from one triangle to another

     is readily computable if the triangle is expressed in terms of a

     $4x4$ matrix.

 

     #+BEGIN_LaTeX

     $$

     \begin{bmatrix}

     x_1 & x_2 & x_3 & n_x \\

     y_1 & y_2 & y_3 & n_y \\ 

     z_1 & z_2 & z_3 & n_z \\

     1 & 1 & 1 & 1 

     \end{bmatrix}

     $$

     #+END_LaTeX

     

     Here, the first three columns of the matrix are the vertices of

     the triangle. The last column is the right-handed unit normal of

     the triangle.

     

     With two triangles $T_{1}$ and $T_{2}$ each expressed as a

     matrix like above, the affine transform from $T_{1}$ to $T_{2}$

     is $T_{2}T_{1}^{-1}$.

     

     The clojure code below recapitulates the formulas above, using

     jMonkeyEngine's =Matrix4f= objects, which can describe any affine

     transformation.

 

     #+caption: Program to interpret triangles as affine transforms.

     #+name: triangle-affine

     #+begin_listing clojure

     #+BEGIN_SRC clojure

 (defn triangle->matrix4f

   "Converts the triangle into a 4x4 matrix: The first three columns

    contain the vertices of the triangle; the last contains the unit

    normal of the triangle. The bottom row is filled with 1s."

   [#^Triangle t]

   (let [mat (Matrix4f.)

         [vert-1 vert-2 vert-3]

         (mapv #(.get t %) (range 3))

         unit-normal (do (.calculateNormal t)(.getNormal t))

         vertices [vert-1 vert-2 vert-3 unit-normal]]

     (dorun 

      (for [row (range 4) col (range 3)]

        (do

          (.set mat col row (.get (vertices row) col))

          (.set mat 3 row 1)))) mat))

 

 (defn triangles->affine-transform

   "Returns the affine transformation that converts each vertex in the

    first triangle into the corresponding vertex in the second

    triangle."

   [#^Triangle tri-1 #^Triangle tri-2]

   (.mult 

    (triangle->matrix4f tri-2)

    (.invert (triangle->matrix4f tri-1))))

     #+END_SRC

     #+end_listing

 

 *** Triangle Boundaries

   

 For efficiency's sake I will divide the tactile-profile image into

 small squares which inscribe each pixel-triangle, then extract the

 points which lie inside the triangle and map them to 3D-space using

 =triangle-transform= above. To do this I need a function,

 =convex-bounds= which finds the smallest box which inscribes a 2D

 triangle.

 

 =inside-triangle?= determines whether a point is inside a triangle

 in 2D pixel-space.

 

     #+caption: Program to efficiently determine point inclusion 

     #+caption: in a triangle.

     #+name: in-triangle

     #+begin_listing clojure

     #+BEGIN_SRC clojure

 (defn convex-bounds

   "Returns the smallest square containing the given vertices, as a

    vector of integers [left top width height]."

   [verts]

   (let [xs (map first verts)

         ys (map second verts)

         x0 (Math/floor (apply min xs))

         y0 (Math/floor (apply min ys))

         x1 (Math/ceil (apply max xs))

         y1 (Math/ceil (apply max ys))]

     [x0 y0 (- x1 x0) (- y1 y0)]))

 

 (defn same-side?

   "Given the points p1 and p2 and the reference point ref, is point p

   on the same side of the line that goes through p1 and p2 as ref is?" 

   [p1 p2 ref p]

   (<=

    0

    (.dot 

     (.cross (.subtract p2 p1) (.subtract p p1))

     (.cross (.subtract p2 p1) (.subtract ref p1)))))

 

 (defn inside-triangle?

   "Is the point inside the triangle?"

   {:author "Dylan Holmes"}

   [#^Triangle tri #^Vector3f p]

   (let [[vert-1 vert-2 vert-3] [(.get1 tri) (.get2 tri) (.get3 tri)]]

     (and

      (same-side? vert-1 vert-2 vert-3 p)

      (same-side? vert-2 vert-3 vert-1 p)

      (same-side? vert-3 vert-1 vert-2 p))))

     #+END_SRC

     #+end_listing

 

 *** Feeler Coordinates

 

     The triangle-related functions above make short work of

     calculating the positions and orientations of each feeler in

     world-space.

 

     #+caption: Program to get the coordinates of ``feelers '' in 

     #+caption: both world and UV-coordinates.

     #+name: feeler-coordinates

     #+begin_listing clojure

     #+BEGIN_SRC clojure

 (defn feeler-pixel-coords

  "Returns the coordinates of the feelers in pixel space in lists, one

   list for each triangle, ordered in the same way as (triangles) and

   (pixel-triangles)."

  [#^Geometry geo image]

  (map 

   (fn [pixel-triangle]

     (filter

      (fn [coord]

        (inside-triangle? (->triangle pixel-triangle)

                          (->vector3f coord)))

        (white-coordinates image (convex-bounds pixel-triangle))))

   (pixel-triangles geo image)))

 

 (defn feeler-world-coords 

  "Returns the coordinates of the feelers in world space in lists, one

   list for each triangle, ordered in the same way as (triangles) and

   (pixel-triangles)."

  [#^Geometry geo image]

  (let [transforms

        (map #(triangles->affine-transform

               (->triangle %1) (->triangle %2))

             (pixel-triangles geo image)

             (triangles geo))]

    (map (fn [transform coords]

           (map #(.mult transform (->vector3f %)) coords))

         transforms (feeler-pixel-coords geo image))))

     #+END_SRC

     #+end_listing

 

     #+caption: Program to get the position of the base and tip of 

     #+caption: each ``feeler''

     #+name: feeler-tips

     #+begin_listing clojure

     #+BEGIN_SRC clojure

 (defn feeler-origins

   "The world space coordinates of the root of each feeler."

   [#^Geometry geo image]

    (reduce concat (feeler-world-coords geo image)))

 

 (defn feeler-tips

   "The world space coordinates of the tip of each feeler."

   [#^Geometry geo image]

   (let [world-coords (feeler-world-coords geo image)

         normals

         (map

          (fn [triangle]

            (.calculateNormal triangle)

            (.clone (.getNormal triangle)))

          (map ->triangle (triangles geo)))]

 

     (mapcat (fn [origins normal]

               (map #(.add % normal) origins))

             world-coords normals)))

 

 (defn touch-topology

   [#^Geometry geo image]

   (collapse (reduce concat (feeler-pixel-coords geo image))))

     #+END_SRC

     #+end_listing

 

 *** Simulated Touch

 

     Now that the functions to construct feelers are complete,

     =touch-kernel= generates functions to be called from within a

     simulation that perform the necessary physics collisions to

     collect tactile data, and =touch!= recursively applies it to every

     node in the creature.

 

     #+caption: Efficient program to transform a ray from 

     #+caption: one position to another.

     #+name: set-ray

     #+begin_listing clojure

     #+BEGIN_SRC clojure

 (defn set-ray [#^Ray ray #^Matrix4f transform

                #^Vector3f origin #^Vector3f tip]

   ;; Doing everything locally reduces garbage collection by enough to

   ;; be worth it.

   (.mult transform origin (.getOrigin ray))

   (.mult transform tip (.getDirection ray))

   (.subtractLocal (.getDirection ray) (.getOrigin ray))

   (.normalizeLocal (.getDirection ray)))

     #+END_SRC

     #+end_listing

 

     #+caption: This is the core of touch in =CORTEX= each feeler 

     #+caption: follows the object it is bound to, reporting any 

     #+caption: collisions that may happen.

     #+name: touch-kernel

     #+begin_listing clojure

     #+BEGIN_SRC clojure

 (defn touch-kernel

   "Constructs a function which will return tactile sensory data from

    'geo when called from inside a running simulation"

   [#^Geometry geo]

   (if-let

       [profile (tactile-sensor-profile geo)]

     (let [ray-reference-origins (feeler-origins geo profile)

           ray-reference-tips (feeler-tips geo profile)

           ray-length (tactile-scale geo)

           current-rays (map (fn [_] (Ray.)) ray-reference-origins)

           topology (touch-topology geo profile)

           correction (float (* ray-length -0.2))]

       ;; slight tolerance for very close collisions.

       (dorun

        (map (fn [origin tip]

               (.addLocal origin (.mult (.subtract tip origin)

                                        correction)))

             ray-reference-origins ray-reference-tips))

       (dorun (map #(.setLimit % ray-length) current-rays))

       (fn [node]

         (let [transform (.getWorldMatrix geo)]

           (dorun

            (map (fn [ray ref-origin ref-tip]

                   (set-ray ray transform ref-origin ref-tip))

                 current-rays ray-reference-origins

                 ray-reference-tips))

           (vector

            topology

            (vec

             (for [ray current-rays]

               (do

                 (let [results (CollisionResults.)]

                   (.collideWith node ray results)

                   (let [touch-objects

                         (filter #(not (= geo (.getGeometry %)))

                                 results)

                         limit (.getLimit ray)]

                     [(if (empty? touch-objects)

                        limit

                        (let [response

                              (apply min (map #(.getDistance %)

                                              touch-objects))]

                          (FastMath/clamp

                           (float 

                            (if (> response limit) (float 0.0)

                                (+ response correction)))

                            (float 0.0)

                            limit)))

                      limit])))))))))))

     #+END_SRC

     #+end_listing

 

     Armed with the =touch!= function, =CORTEX= becomes capable of

     giving creatures a sense of touch. A simple test is to create a

     cube that is outfitted with a uniform distribution of touch

     sensors. It can feel the ground and any balls that it touches.

 

     #+caption: =CORTEX= interface for creating touch in a simulated

     #+caption: creature.

     #+name: touch

     #+begin_listing clojure

     #+BEGIN_SRC clojure

 (defn touch! 

   "Endow the creature with the sense of touch. Returns a sequence of

    functions, one for each body part with a tactile-sensor-profile,

    each of which when called returns sensory data for that body part."

   [#^Node creature]

   (filter

    (comp not nil?)

    (map touch-kernel

         (filter #(isa? (class %) Geometry)

                 (node-seq creature)))))

     #+END_SRC

     #+end_listing

     

     The tactile-sensor-profile image for the touch cube is a simple

     cross with a uniform distribution of touch sensors:

 

     #+caption: The touch profile for the touch-cube. Each pure white 

     #+caption: pixel defines a touch sensitive feeler.

     #+name: touch-cube-uv-map

     #+ATTR_LaTeX: :width 7cm

     [[./images/touch-profile.png]]

 

     #+caption: The touch cube reacts to cannonballs. The black, red, 

     #+caption: and white cross on the right is a visual display of 

     #+caption: the creature's touch. White means that it is feeling 

     #+caption: something strongly, black is not feeling anything,

     #+caption: and gray is in-between. The cube can feel both the 

     #+caption: floor and the ball. Notice that when the ball causes 

     #+caption: the cube to tip, that the bottom face can still feel 

     #+caption: part of the ground.

     #+name: touch-cube-uv-map-2

     #+ATTR_LaTeX: :width 15cm

     [[./images/touch-cube.png]]

 

 ** Proprioception provides knowledge of your own body's position

 

    Close your eyes, and touch your nose with your right index finger.

    How did you do it? You could not see your hand, and neither your

    hand nor your nose could use the sense of touch to guide the path

    of your hand. There are no sound cues, and Taste and Smell

    certainly don't provide any help. You know where your hand is

    without your other senses because of Proprioception.

    

    Humans can sometimes loose this sense through viral infections or

    damage to the spinal cord or brain, and when they do, they loose

    the ability to control their own bodies without looking directly at

    the parts they want to move. In [[http://en.wikipedia.org/wiki/The_Man_Who_Mistook_His_Wife_for_a_Hat][The Man Who Mistook His Wife for a

    Hat]] (\cite{man-wife-hat}), a woman named Christina looses this

    sense and has to learn how to move by carefully watching her arms

    and legs. She describes proprioception as the "eyes of the body,

    the way the body sees itself".

    

    Proprioception in humans is mediated by [[http://en.wikipedia.org/wiki/Articular_capsule][joint capsules]], [[http://en.wikipedia.org/wiki/Muscle_spindle][muscle

    spindles]], and the [[http://en.wikipedia.org/wiki/Golgi_tendon_organ][Golgi tendon organs]]. These measure the relative

    positions of each body part by monitoring muscle strain and length.

    

    It's clear that this is a vital sense for fluid, graceful movement.

    It's also particularly easy to implement in jMonkeyEngine.

    

    My simulated proprioception calculates the relative angles of each

    joint from the rest position defined in the Blender file. This

    simulates the muscle-spindles and joint capsules. I will deal with

    Golgi tendon organs, which calculate muscle strain, in the next

    chapter.

 

 *** Helper functions

 

     =absolute-angle= calculates the angle between two vectors,

     relative to a third axis vector. This angle is the number of

     radians you have to move counterclockwise around the axis vector

     to get from the first to the second vector. It is not commutative

     like a normal dot-product angle is.

 

     The purpose of these functions is to build a system of angle

     measurement that is biologically plausible.

 

     #+caption: Program to measure angles along a vector

     #+name: helpers

     #+begin_listing clojure

     #+BEGIN_SRC clojure

 (defn right-handed?

   "true iff the three vectors form a right handed coordinate

    system. The three vectors do not have to be normalized or

    orthogonal."

   [vec1 vec2 vec3]

   (pos? (.dot (.cross vec1 vec2) vec3)))

 

 (defn absolute-angle

   "The angle between 'vec1 and 'vec2 around 'axis. In the range 

    [0 (* 2 Math/PI)]."

   [vec1 vec2 axis]

   (let [angle (.angleBetween vec1 vec2)]

     (if (right-handed? vec1 vec2 axis)

       angle (- (* 2 Math/PI) angle))))

     #+END_SRC

     #+end_listing

 

 *** Proprioception Kernel

     

     Given a joint, =proprioception-kernel= produces a function that

     calculates the Euler angles between the objects the joint

     connects. The only tricky part here is making the angles relative

     to the joint's initial ``straightness''.

 

     #+caption: Program to return biologically reasonable proprioceptive

     #+caption: data for each joint.

     #+name: proprioception

     #+begin_listing clojure

     #+BEGIN_SRC clojure

 (defn proprioception-kernel

   "Returns a function which returns proprioceptive sensory data when

   called inside a running simulation."

   [#^Node parts #^Node joint]

   (let [[obj-a obj-b] (joint-targets parts joint)

         joint-rot (.getWorldRotation joint)

         x0 (.mult joint-rot Vector3f/UNIT_X)

         y0 (.mult joint-rot Vector3f/UNIT_Y)

         z0 (.mult joint-rot Vector3f/UNIT_Z)]

     (fn []

       (let [rot-a (.clone (.getWorldRotation obj-a))

             rot-b (.clone (.getWorldRotation obj-b))

             x (.mult rot-a x0)

             y (.mult rot-a y0)

             z (.mult rot-a z0)

 

             X (.mult rot-b x0)

             Y (.mult rot-b y0)

             Z (.mult rot-b z0)

             heading  (Math/atan2 (.dot X z) (.dot X x))

             pitch  (Math/atan2 (.dot X y) (.dot X x))

 

             ;; rotate x-vector back to origin

             reverse

             (doto (Quaternion.)

               (.fromAngleAxis

                (.angleBetween X x)

                (let [cross (.normalize (.cross X x))]

                  (if (= 0 (.length cross)) y cross))))

             roll (absolute-angle (.mult reverse Y) y x)]

         [heading pitch roll]))))

 

 (defn proprioception!

   "Endow the creature with the sense of proprioception. Returns a

    sequence of functions, one for each child of the \"joints\" node in

    the creature, which each report proprioceptive information about

    that joint."

   [#^Node creature]

   ;; extract the body's joints

   (let [senses (map (partial proprioception-kernel creature)

                     (joints creature))]

     (fn []

       (map #(%) senses))))

     #+END_SRC

     #+end_listing

 

     =proprioception!= maps =proprioception-kernel= across all the

     joints of the creature. It uses the same list of joints that

     =joints= uses. Proprioception is the easiest sense to implement in

     =CORTEX=, and it will play a crucial role when efficiently

     implementing empathy.

 

     #+caption: In the upper right corner, the three proprioceptive

     #+caption: angle measurements are displayed. Red is yaw, Green is 

     #+caption: pitch, and White is roll.

     #+name: proprio

     #+ATTR_LaTeX: :width 11cm

     [[./images/proprio.png]]

 

 ** Muscles contain both sensors and effectors

 

    Surprisingly enough, terrestrial creatures only move by using

    torque applied about their joints. There's not a single straight

    line of force in the human body at all! (A straight line of force

    would correspond to some sort of jet or rocket propulsion.)

    

    In humans, muscles are composed of muscle fibers which can contract

    to exert force. The muscle fibers which compose a muscle are

    partitioned into discrete groups which are each controlled by a

    single alpha motor neuron. A single alpha motor neuron might

    control as little as three or as many as one thousand muscle

    fibers. When the alpha motor neuron is engaged by the spinal cord,

    it activates all of the muscle fibers to which it is attached. The

    spinal cord generally engages the alpha motor neurons which control

    few muscle fibers before the motor neurons which control many

    muscle fibers. This recruitment strategy allows for precise

    movements at low strength. The collection of all motor neurons that

    control a muscle is called the motor pool. The brain essentially

    says "activate 30% of the motor pool" and the spinal cord recruits

    motor neurons until 30% are activated. Since the distribution of

    power among motor neurons is unequal and recruitment goes from

    weakest to strongest, the first 30% of the motor pool might be 5%

    of the strength of the muscle.

    

    My simulated muscles follow a similar design: Each muscle is

    defined by a 1-D array of numbers (the "motor pool"). Each entry in

    the array represents a motor neuron which controls a number of

    muscle fibers equal to the value of the entry. Each muscle has a

    scalar strength factor which determines the total force the muscle

    can exert when all motor neurons are activated. The effector

    function for a muscle takes a number to index into the motor pool,

    and then "activates" all the motor neurons whose index is lower or

    equal to the number. Each motor-neuron will apply force in

    proportion to its value in the array. Lower values cause less

    force. The lower values can be put at the "beginning" of the 1-D

    array to simulate the layout of actual human muscles, which are

    capable of more precise movements when exerting less force. Or, the

    motor pool can simulate more exotic recruitment strategies which do

    not correspond to human muscles.

    

    This 1D array is defined in an image file for ease of

    creation/visualization. Here is an example muscle profile image.

 

    #+caption: A muscle profile image that describes the strengths

    #+caption: of each motor neuron in a muscle. White is weakest 

    #+caption: and dark red is strongest. This particular pattern 

    #+caption: has weaker motor neurons at the beginning, just 

    #+caption: like human muscle.

    #+name: muscle-recruit

    #+ATTR_LaTeX: :width 7cm

    [[./images/basic-muscle.png]]

 

 *** Muscle meta-data

 

     #+caption: Program to deal with loading muscle data from a Blender

     #+caption: file's metadata.

     #+name: motor-pool

     #+begin_listing clojure

     #+BEGIN_SRC clojure

 (defn muscle-profile-image

   "Get the muscle-profile image from the node's blender meta-data."

   [#^Node muscle]

   (if-let [image (meta-data muscle "muscle")]

     (load-image image)))

 

 (defn muscle-strength

   "Return the strength of this muscle, or 1 if it is not defined."

   [#^Node muscle]

   (if-let [strength (meta-data muscle "strength")]

     strength 1))

 

 (defn motor-pool

   "Return a vector where each entry is the strength of the \"motor

    neuron\" at that part in the muscle."

   [#^Node muscle]

   (let [profile (muscle-profile-image muscle)]

     (vec

      (let [width (.getWidth profile)]

        (for [x (range width)]

        (- 255

           (bit-and

            0x0000FF

            (.getRGB profile x 0))))))))

     #+END_SRC

     #+end_listing

 

     Of note here is =motor-pool= which interprets the muscle-profile

     image in a way that allows me to use gradients between white and

     red, instead of shades of gray as I've been using for all the

     other senses. This is purely an aesthetic touch.

 

 *** Creating muscles

 

     #+caption: This is the core movement function in =CORTEX=, which

     #+caption: implements muscles that report on their activation.

     #+name: muscle-kernel

     #+begin_listing clojure

     #+BEGIN_SRC clojure

 (defn movement-kernel

   "Returns a function which when called with a integer value inside a

    running simulation will cause movement in the creature according

    to the muscle's position and strength profile. Each function

    returns the amount of force applied / max force."

   [#^Node creature #^Node muscle]

   (let [target (closest-node creature muscle)

         axis

         (.mult (.getWorldRotation muscle) Vector3f/UNIT_Y)

         strength (muscle-strength muscle)

         

         pool (motor-pool muscle)

         pool-integral (reductions + pool)

         forces

         (vec (map  #(float (* strength (/ % (last pool-integral))))

               pool-integral))

         control (.getControl target RigidBodyControl)]

     (fn [n]

       (let [pool-index (max 0 (min n (dec (count pool))))

             force (forces pool-index)]

         (.applyTorque control (.mult axis force))

         (float (/ force strength))))))

 

 (defn movement!

   "Endow the creature with the power of movement. Returns a sequence

    of functions, each of which accept an integer value and will

    activate their corresponding muscle."

   [#^Node creature]

     (for [muscle (muscles creature)]

       (movement-kernel creature muscle)))

     #+END_SRC

     #+end_listing

 

 

     =movement-kernel= creates a function that controls the movement

     of the nearest physical node to the muscle node. The muscle exerts

     a rotational force dependent on it's orientation to the object in

     the Blender file. The function returned by =movement-kernel= is

     also a sense function: it returns the percent of the total muscle

     strength that is currently being employed. This is analogous to

     muscle tension in humans and completes the sense of proprioception

     begun in the last chapter.

     

 ** =CORTEX= brings complex creatures to life!

    

    The ultimate test of =CORTEX= is to create a creature with the full

    gamut of senses and put it though its paces. 

 

    With all senses enabled, my right hand model looks like an

    intricate marionette hand with several strings for each finger:

 

    #+caption: View of the hand model with all sense nodes. You can see 

    #+caption: the joint, muscle, ear, and eye nodes here.

    #+name: hand-nodes-1

    #+ATTR_LaTeX: :width 11cm

    [[./images/hand-with-all-senses2.png]]

 

    #+caption: An alternate view of the hand.

    #+name: hand-nodes-2

    #+ATTR_LaTeX: :width 15cm

    [[./images/hand-with-all-senses3.png]]

 

    With the hand fully rigged with senses, I can run it though a test

    that will test everything. 

 

    #+caption: Selected frames from a full test of the hand with all

    #+caption: senses. Note especially the interactions the hand has

    #+caption: with itself: it feels its own palm and fingers, and when

    #+caption: it curls its fingers, it sees them with its eye (which

    #+caption: is located in the center of the palm. The red block

    #+caption: appears with a pure tone sound. The hand then uses its

    #+caption: muscles to launch the cube!

    #+name: integration

    #+ATTR_LaTeX: :width 15cm

    [[./images/integration.png]]

 

 ** =CORTEX= enables many possibilities for further research

 

    Often times, the hardest part of building a system involving

    creatures is dealing with physics and graphics. =CORTEX= removes

    much of this initial difficulty and leaves researchers free to

    directly pursue their ideas. I hope that even undergrads with a

    passing curiosity about simulated touch or creature evolution will

    be able to use cortex for experimentation. =CORTEX= is a completely

    simulated world, and far from being a disadvantage, its simulated

    nature enables you to create senses and creatures that would be

    impossible to make in the real world.

 

    While not by any means a complete list, here are some paths

    =CORTEX= is well suited to help you explore:

 

    - Empathy         :: my empathy program leaves many areas for

         improvement, among which are using vision to infer

         proprioception and looking up sensory experience with imagined

         vision, touch, and sound.

    - Evolution :: Karl Sims created a rich environment for simulating

         the evolution of creatures on a Connection Machine

         (\cite{sims-evolving-creatures}). Today, this can be redone

         and expanded with =CORTEX= on an ordinary computer.

    - Exotic senses  :: Cortex enables many fascinating senses that are

         not possible to build in the real world. For example,

         telekinesis is an interesting avenue to explore. You can also

         make a ``semantic'' sense which looks up metadata tags on

         objects in the environment the metadata tags might contain

         other sensory information.

    - Imagination via subworlds :: this would involve a creature with

         an effector which creates an entire new sub-simulation where

         the creature has direct control over placement/creation of

         objects via simulated telekinesis. The creature observes this

         sub-world through its normal senses and uses its observations

         to make predictions about its top level world.

    - Simulated prescience :: step the simulation forward a few ticks,

         gather sensory data, then supply this data for the creature as

         one of its actual senses. The cost of prescience is slowing

         the simulation down by a factor proportional to however far

         you want the entities to see into the future. What happens

         when two evolved creatures that can each see into the future

         fight each other?

    - Swarm creatures :: Program a group of creatures that cooperate

         with each other. Because the creatures would be simulated, you

         could investigate computationally complex rules of behavior

         which still, from the group's point of view, would happen in

         real time. Interactions could be as simple as cellular

         organisms communicating via flashing lights, or as complex as

         humanoids completing social tasks, etc.

    - =HACKER= for writing muscle-control programs :: Presented with a

         low-level muscle control / sense API, generate higher level

         programs for accomplishing various stated goals. Example goals

         might be "extend all your fingers" or "move your hand into the

         area with blue light" or "decrease the angle of this joint".

         It would be like Sussman's HACKER, except it would operate

         with much more data in a more realistic world. Start off with

         "calisthenics" to develop subroutines over the motor control

         API. The low level programming code might be a turning machine

         that could develop programs to iterate over a "tape" where

         each entry in the tape could control recruitment of the fibers

         in a muscle.

    - Sense fusion :: There is much work to be done on sense

         integration -- building up a coherent picture of the world and

         the things in it. With =CORTEX= as a base, you can explore

         concepts like self-organizing maps or cross modal clustering

         in ways that have never before been tried.

    - Inverse kinematics :: experiments in sense guided motor control

         are easy given =CORTEX='s support -- you can get right to the

         hard control problems without worrying about physics or

         senses.

 

 \newpage

 

 * =EMPATH=: action recognition in a simulated worm

 

   Here I develop a computational model of empathy, using =CORTEX= as a

   base. Empathy in this context is the ability to observe another

   creature and infer what sorts of sensations that creature is

   feeling. My empathy algorithm involves multiple phases. First is

   free-play, where the creature moves around and gains sensory

   experience. From this experience I construct a representation of the

   creature's sensory state space, which I call \Phi-space. Using

   \Phi-space, I construct an efficient function which takes the

   limited data that comes from observing another creature and enriches

   it with a full compliment of imagined sensory data. I can then use

   the imagined sensory data to recognize what the observed creature is

   doing and feeling, using straightforward embodied action predicates.

   This is all demonstrated with using a simple worm-like creature, and

   recognizing worm-actions based on limited data.

 

   #+caption: Here is the worm with which we will be working. It is

   #+caption: composed of 5 segments. Each segment has a pair of

   #+caption: extensor and flexor muscles. Each of the worm's four

   #+caption: joints is a hinge joint which allows about 30 degrees of

   #+caption: rotation to either side. Each segment of the worm is

   #+caption: touch-capable and has a uniform distribution of touch

   #+caption: sensors on each of its faces. Each joint has a

   #+caption: proprioceptive sense to detect relative positions. The

   #+caption: worm segments are all the same except for the first one,

   #+caption: which has a much higher weight than the others to allow

   #+caption: for easy manual motor control.

   #+name: basic-worm-view

   #+ATTR_LaTeX: :width 10cm

   [[./images/basic-worm-view.png]]

 

   #+caption: Program for reading a worm from a Blender file and 

   #+caption: outfitting it with the senses of proprioception, 

   #+caption: touch, and the ability to move, as specified in the 

   #+caption: Blender file.

   #+name: get-worm

   #+begin_listing clojure

   #+begin_src clojure

 (defn worm []

   (let [model (load-blender-model "Models/worm/worm.blend")]

     {:body (doto model (body!))

      :touch (touch! model)

      :proprioception (proprioception! model)

      :muscles (movement! model)}))

   #+end_src

   #+end_listing

 

 ** Embodiment factors action recognition into manageable parts

 

    Using empathy, I divide the problem of action recognition into a

    recognition process expressed in the language of a full compliment

    of senses, and an imaginative process that generates full sensory

    data from partial sensory data. Splitting the action recognition

    problem in this manner greatly reduces the total amount of work to

    recognize actions: The imaginative process is mostly just matching

    previous experience, and the recognition process gets to use all

    the senses to directly describe any action.

 

 ** Action recognition is easy with a full gamut of senses

 

    Embodied representation using multiple senses such as touch,

    proprioception, and muscle tension turns out be exceedingly

    efficient at describing body-centered actions. It is the right

    language for the job. For example, it takes only around 5 lines of

    clojure code to describe the action of curling using embodied

    primitives. It takes about 10 lines to describe the seemingly

    complicated action of wiggling.

 

    The following action predicates each take a stream of sensory

    experience, observe however much of it they desire, and decide

    whether the worm is doing the action they describe. =curled?=

    relies on proprioception, =resting?= relies on touch, =wiggling?=

    relies on a Fourier analysis of muscle contraction, and

    =grand-circle?= relies on touch and reuses =curled?= in its

    definition, showing how embodied predicates can be composed.

    

    

    #+caption: Program for detecting whether the worm is curled. This is the 

    #+caption: simplest action predicate, because it only uses the last frame 

    #+caption: of sensory experience, and only uses proprioceptive data. Even 

    #+caption: this simple predicate, however, is automatically frame 

    #+caption: independent and ignores vermopomorphic\protect\footnotemark  

    #+caption: \space differences such as worm textures and colors.

    #+name: curled

    #+begin_listing clojure

    #+begin_src clojure

 (defn curled?

   "Is the worm curled up?"

   [experiences]

   (every?

    (fn [[_ _ bend]]

      (> (Math/sin bend) 0.64))

    (:proprioception (peek experiences))))

    #+end_src

    #+end_listing

    

    #+BEGIN_LaTeX

    \footnotetext{Like \emph{anthropomorphic} except for worms instead of humans.}

    #+END_LaTeX

 

    #+caption: Program for summarizing the touch information in a patch 

    #+caption: of skin.

    #+name: touch-summary

    #+begin_listing clojure

    #+begin_src clojure

 (defn contact

   "Determine how much contact a particular worm segment has with

    other objects. Returns a value between 0 and 1, where 1 is full

    contact and 0 is no contact."

   [touch-region [coords contact :as touch]]

   (-> (zipmap coords contact)

       (select-keys touch-region)

       (vals)

       (#(map first %))

       (average)

       (* 10)

       (- 1)

       (Math/abs)))

    #+end_src

    #+end_listing

 

 

    #+caption: Program for detecting whether the worm is at rest. This program

    #+caption: uses a summary of the tactile information from the underbelly 

    #+caption: of the worm, and is only true if every segment is touching the 

    #+caption: floor. Note that this function contains no references to 

    #+caption: proprioception at all.

    #+name: resting

 #+begin_listing clojure

    #+begin_src clojure

 (def worm-segment-bottom (rect-region [8 15] [14 22]))

 

 (defn resting?

   "Is the worm resting on the ground?"

   [experiences]

   (every?

    (fn [touch-data]

      (< 0.9 (contact worm-segment-bottom touch-data)))

    (:touch (peek experiences))))

    #+end_src

    #+end_listing

 

    #+caption: Program for detecting whether the worm is curled up into a 

    #+caption: full circle. Here the embodied approach begins to shine, as

    #+caption: I am able to both use a previous action predicate (=curled?=)

    #+caption: as well as the direct tactile experience of the head and tail.

    #+name: grand-circle

 #+begin_listing clojure

    #+begin_src clojure

 (def worm-segment-bottom-tip (rect-region [15 15] [22 22]))

 

 (def worm-segment-top-tip (rect-region [0 15] [7 22]))

 

 (defn grand-circle?

   "Does the worm form a majestic circle (one end touching the other)?"

   [experiences]

   (and (curled? experiences)

        (let [worm-touch (:touch (peek experiences))

              tail-touch (worm-touch 0)

              head-touch (worm-touch 4)]

          (and (< 0.55 (contact worm-segment-bottom-tip tail-touch))

               (< 0.55 (contact worm-segment-top-tip    head-touch))))))

    #+end_src

    #+end_listing

 

 

    #+caption: Program for detecting whether the worm has been wiggling for 

    #+caption: the last few frames. It uses a Fourier analysis of the muscle 

    #+caption: contractions of the worm's tail to determine wiggling. This is 

    #+caption: significant because there is no particular frame that clearly 

    #+caption: indicates that the worm is wiggling --- only when multiple frames 

    #+caption: are analyzed together is the wiggling revealed. Defining 

    #+caption: wiggling this way also gives the worm an opportunity to learn 

    #+caption: and recognize ``frustrated wiggling'', where the worm tries to 

    #+caption: wiggle but can't. Frustrated wiggling is very visually different 

    #+caption: from actual wiggling, but this definition gives it to us for free.

    #+name: wiggling

 #+begin_listing clojure

    #+begin_src clojure

 (defn fft [nums]

   (map

    #(.getReal %)

    (.transform

     (FastFourierTransformer. DftNormalization/STANDARD)

     (double-array nums) TransformType/FORWARD)))

 

 (def indexed (partial map-indexed vector))

 

 (defn max-indexed [s]

   (first (sort-by (comp - second) (indexed s))))

 

 (defn wiggling?

   "Is the worm wiggling?"

   [experiences]

   (let [analysis-interval 0x40]

     (when (> (count experiences) analysis-interval)

       (let [a-flex 3

             a-ex   2

             muscle-activity

             (map :muscle (vector:last-n experiences analysis-interval))

             base-activity

             (map #(- (% a-flex) (% a-ex)) muscle-activity)]

         (= 2

            (first

             (max-indexed

              (map #(Math/abs %)

                   (take 20 (fft base-activity))))))))))

    #+end_src

    #+end_listing

 

    With these action predicates, I can now recognize the actions of

    the worm while it is moving under my control and I have access to

    all the worm's senses.

 

    #+caption: Use the action predicates defined earlier to report on 

    #+caption: what the worm is doing while in simulation.

    #+name: report-worm-activity

 #+begin_listing clojure

    #+begin_src clojure

 (defn debug-experience

   [experiences text]

   (cond

    (grand-circle? experiences) (.setText text "Grand Circle")

    (curled? experiences)       (.setText text "Curled")

    (wiggling? experiences)     (.setText text "Wiggling")

    (resting? experiences)      (.setText text "Resting")))

    #+end_src

    #+end_listing

 

    #+caption: Using =debug-experience=, the body-centered predicates

    #+caption: work together to classify the behavior of the worm. 

    #+caption: the predicates are operating with access to the worm's

    #+caption: full sensory data.

    #+name: basic-worm-view

    #+ATTR_LaTeX: :width 10cm

    [[./images/worm-identify-init.png]]

 

    These action predicates satisfy the recognition requirement of an

    empathic recognition system. There is power in the simplicity of

    the action predicates. They describe their actions without getting

    confused in visual details of the worm. Each one is independent of

    position and rotation, but more than that, they are each

    independent of irrelevant visual details of the worm and the

    environment. They will work regardless of whether the worm is a

    different color or heavily textured, or if the environment has

    strange lighting.

 

    Consider how the human act of jumping might be described with

    body-centered action predicates: You might specify that jumping is

    mainly the feeling of your knees bending, your thigh muscles

    contracting, and your inner ear experiencing a certain sort of back

    and forth acceleration. This representation is a very concrete

    description of jumping, couched in terms of muscles and senses, but

    it also has the ability to describe almost all kinds of jumping, a

    generality that you might think could only be achieved by a very

    abstract description. The body centered jumping predicate does not

    have terms that consider the color of a person's skin or whether

    they are male or female, instead it gets right to the meat of what

    jumping actually /is/.

 

    Of course, the action predicates are not directly applicable to

    video data, which lacks the advanced sensory information which they

    require!

 

    The trick now is to make the action predicates work even when the

    sensory data on which they depend is absent!

 

 ** \Phi-space describes the worm's experiences

    

    As a first step towards building empathy, I need to gather all of

    the worm's experiences during free play. I use a simple vector to

    store all the experiences. 

 

    Each element of the experience vector exists in the vast space of

    all possible worm-experiences. Most of this vast space is actually

    unreachable due to physical constraints of the worm's body. For

    example, the worm's segments are connected by hinge joints that put

    a practical limit on the worm's range of motions without limiting

    its degrees of freedom. Some groupings of senses are impossible;

    the worm can not be bent into a circle so that its ends are

    touching and at the same time not also experience the sensation of

    touching itself.

 

    As the worm moves around during free play and its experience vector

    grows larger, the vector begins to define a subspace which is all

    the sensations the worm can practically experience during normal

    operation. I call this subspace \Phi-space, short for

    physical-space. The experience vector defines a path through

    \Phi-space. This path has interesting properties that all derive

    from physical embodiment. The proprioceptive components of the path

    vary smoothly, because in order for the worm to move from one

    position to another, it must pass through the intermediate

    positions. The path invariably forms loops as common actions are

    repeated. Finally and most importantly, proprioception alone

    actually gives very strong inference about the other senses. For

    example, when the worm is proprioceptively flat over several

    frames, you can infer that it is touching the ground and that its

    muscles are not active, because if the muscles were active, the

    worm would be moving and would not remain perfectly flat. In order

    to stay flat, the worm has to be touching the ground, or it would

    again be moving out of the flat position due to gravity. If the

    worm is positioned in such a way that it interacts with itself,

    then it is very likely to be feeling the same tactile feelings as

    the last time it was in that position, because it has the same body

    as then. As you observe multiple frames of proprioceptive data, you

    can become increasingly confident about the exact activations of

    the worm's muscles, because it generally takes a unique combination

    of muscle contractions to transform the worm's body along a

    specific path through \Phi-space.

 

    The worm's total life experience is a long looping path through

    \Phi-space. I will now introduce simple way of taking that

    experience path and building a function that can infer complete

    sensory experience given only a stream of proprioceptive data. This

    /empathy/ function will provide a bridge to use the body centered

    action predicates on video-like streams of information.

    

 ** Empathy is the process of building paths in \Phi-space

 

    Here is the core of a basic empathy algorithm, starting with an

    experience vector:

 

    An /experience-index/ is an index into the grand experience vector

    that defines the worm's life. It is a time-stamp for each set of

    sensations the worm has experienced.

 

    First, I group the experience-indices into bins according to the

    similarity of their proprioceptive data. I organize my bins into a

    3 level hierarchy. The smallest bins have an approximate size of

    0.001 radians in all proprioceptive dimensions. Each higher level

    is 10x bigger than the level below it.

 

    The bins serve as a hashing function for proprioceptive data. Given

    a single piece of proprioceptive experience, the bins allow me to

    rapidly find all other similar experience-indices of past

    experience that had a very similar proprioceptive configuration.

    When looking up a proprioceptive experience, if the smallest bin

    does not match any previous experience, then I use successively

    larger bins until a match is found or I reach the largest bin.

    

    Given a sequence of proprioceptive input, I use the bins to

    generate a set of similar experiences for each input using the

    tiered proprioceptive bins.

 

    Finally, to infer sensory data, I select the longest consecutive

    chain of experiences that threads through the sets of similar

    experiences, starting with the current moment as a root and going

    backwards. Consecutive experience means that the experiences appear

    next to each other in the experience vector.

 

    A stream of proprioceptive input might be:

    

    #+BEGIN_EXAMPLE

    [ flat, flat, flat, flat, flat, flat, lift-head ]

    #+END_EXAMPLE

 

    The worm's previous experience of lying on the ground and lifting

    its head generates possible interpretations for each frame (the

    numbers are experience-indices):

 

    #+BEGIN_EXAMPLE

    [ flat, flat, flat, flat, flat, flat, flat, lift-head ]

       1     1     1     1     1     1     1     4     

       2     2     2     2     2     2     2   

       3     3     3     3     3     3     3

       6     6     6     6     6     6     6

       7     7     7     7     7     7     7

       8     8     8     8     8     8     8

       9     9     9     9     9     9     9

    #+END_EXAMPLE

    

    These interpretations suggest a new path through phi space:

    

    #+BEGIN_EXAMPLE

    [ flat, flat, flat, flat, flat, flat, flat, lift-head ]

       6     7     8     9     1     2     3     4

    #+END_EXAMPLE

 

    The new path through \Phi-space is synthesized from two actual

    paths that the creature has experienced: the "1-2-3-4" chain and

    the "6-7-8-9" chain. The "1-2-3-4" chain is necessary because it

    ends with the worm lifting its head. It originated from a short

    training session where the worm rested on the floor for a brief

    while and then raised its head. The "6-7-8-9" chain is part of a

    longer chain of inactivity where the worm simply rested on the

    floor without moving. It is preferred over a "1-2-3" chain (which

    also describes inactivity) because it is longer. The main ideas

    again:

    

    - Imagined \Phi-space paths are synthesized by looping and mixing

      previous experiences.

 

    - Longer experience paths (less edits) are preferred.

 

    - The present is more important than the past --- more recent

      events take precedence in interpretation.

 

    This algorithm has three advantages: 

 

    1. It's simple

 

    3. It's very fast -- retrieving possible interpretations takes

       constant time. Tracing through chains of interpretations takes

       time proportional to the average number of experiences in a

       proprioceptive bin. Redundant experiences in \Phi-space can be

       merged to save computation.

 

    2. It protects from wrong interpretations of transient ambiguous

       proprioceptive data. For example, if the worm is flat for just

       an instant, this flatness will not be interpreted as implying

       that the worm has its muscles relaxed, since the flatness is

       part of a longer chain which includes a distinct pattern of

       muscle activation. Markov chains or other memoryless statistical

       models that operate on individual frames may very well make this

       mistake.

 

    #+caption: Program to convert an experience vector into a 

    #+caption: proprioceptively binned lookup function.

    #+name: bin

 #+begin_listing clojure

    #+begin_src clojure

 (defn bin [digits]

   (fn [angles]

     (->> angles

          (flatten)

          (map (juxt #(Math/sin %) #(Math/cos %)))

          (flatten)

          (mapv #(Math/round (* % (Math/pow 10 (dec digits))))))))

 

 (defn gen-phi-scan 

   "Nearest-neighbors with binning. Only returns a result if

    the proprioceptive data is within 10% of a previously recorded

    result in all dimensions."

   [phi-space]

   (let [bin-keys (map bin [3 2 1])

         bin-maps

         (map (fn [bin-key]

                (group-by

                 (comp bin-key :proprioception phi-space)

                 (range (count phi-space)))) bin-keys)

         lookups (map (fn [bin-key bin-map]

                        (fn [proprio] (bin-map (bin-key proprio))))

                      bin-keys bin-maps)]

     (fn lookup [proprio-data]

       (set (some #(% proprio-data) lookups)))))

    #+end_src

    #+end_listing

 

    #+caption: =longest-thread= finds the longest path of consecutive 

    #+caption: past experiences to explain proprioceptive worm data from 

    #+caption: previous data. Here, the film strip represents the  

    #+caption: creature's previous experience. Sort sequences of

    #+caption: memories are spliced together to match the

    #+caption: proprioceptive data. Their carry the other senses 

    #+caption: along with them.

    #+name: phi-space-history-scan

    #+ATTR_LaTeX: :width 10cm

    [[./images/film-of-imagination.png]]

 

    =longest-thread= infers sensory data by stitching together pieces

    from previous experience. It prefers longer chains of previous

    experience to shorter ones. For example, during training the worm

    might rest on the ground for one second before it performs its

    exercises. If during recognition the worm rests on the ground for

    five seconds, =longest-thread= will accommodate this five second

    rest period by looping the one second rest chain five times.

 

    =longest-thread= takes time proportional to the average number of

    entries in a proprioceptive bin, because for each element in the

    starting bin it performs a series of set lookups in the preceding

    bins. If the total history is limited, then this takes time

    proportional to a only a constant multiple of the number of entries

    in the starting bin. This analysis also applies, even if the action

    requires multiple longest chains -- it's still the average number

    of entries in a proprioceptive bin times the desired chain length.

    Because =longest-thread= is so efficient and simple, I can

    interpret worm-actions in real time.

 

    #+caption: Program to calculate empathy by tracing though \Phi-space

    #+caption: and finding the longest (ie. most coherent) interpretation

    #+caption: of the data.

    #+name: longest-thread

 #+begin_listing clojure

    #+begin_src clojure

 (defn longest-thread

   "Find the longest thread from phi-index-sets. The index sets should

    be ordered from most recent to least recent."

   [phi-index-sets]

   (loop [result '()

          [thread-bases & remaining :as phi-index-sets] phi-index-sets]

     (if (empty? phi-index-sets)

       (vec result)

       (let [threads

             (for [thread-base thread-bases]

               (loop [thread (list thread-base)

                      remaining remaining]

                 (let [next-index (dec (first thread))]

                   (cond (empty? remaining) thread

                         (contains? (first remaining) next-index)

                         (recur

                          (cons next-index thread) (rest remaining))

                         :else thread))))

             longest-thread

             (reduce (fn [thread-a thread-b]

                       (if (> (count thread-a) (count thread-b))

                         thread-a thread-b))

                     '(nil)

                     threads)]

         (recur (concat longest-thread result)

                (drop (count longest-thread) phi-index-sets))))))

    #+end_src

    #+end_listing

 

    There is one final piece, which is to replace missing sensory data

    with a best-guess estimate. While I could fill in missing data by

    using a gradient over the closest known sensory data points,

    averages can be misleading. It is certainly possible to create an

    impossible sensory state by averaging two possible sensory states.

    For example, consider moving your hand in an arc over your head. If

    for some reason you only have the initial and final positions of

    this movement in your \Phi-space, averaging them together will

    produce the proprioceptive sensation of having your hand /inside/

    your head, which is physically impossible to ever experience

    (barring motor adaption illusions). Therefore I simply replicate

    the most recent sensory experience to fill in the gaps.

 

    #+caption: Fill in blanks in sensory experience by replicating the most 

    #+caption: recent experience.

    #+name: infer-nils

 #+begin_listing clojure

    #+begin_src clojure

 (defn infer-nils

   "Replace nils with the next available non-nil element in the

    sequence, or barring that, 0."

   [s]

   (loop [i (dec (count s))

          v (transient s)]

     (if (zero? i) (persistent! v)

         (if-let [cur (v i)]

           (if (get v (dec i) 0)

             (recur (dec i) v)

             (recur (dec i) (assoc! v (dec i) cur)))

           (recur i (assoc! v i 0))))))

    #+end_src

    #+end_listing

   

 ** =EMPATH= recognizes actions efficiently

    

    To use =EMPATH= with the worm, I first need to gather a set of

    experiences from the worm that includes the actions I want to

    recognize. The =generate-phi-space= program (listing

    \ref{generate-phi-space} runs the worm through a series of

    exercises and gathers those experiences into a vector. The

    =do-all-the-things= program is a routine expressed in a simple

    muscle contraction script language for automated worm control. It

    causes the worm to rest, curl, and wiggle over about 700 frames

    (approx. 11 seconds).

 

    #+caption: Program to gather the worm's experiences into a vector for 

    #+caption: further processing. The =motor-control-program= line uses

    #+caption: a motor control script that causes the worm to execute a series

    #+caption: of ``exercises'' that include all the action predicates.

    #+name: generate-phi-space

 #+begin_listing clojure 

    #+begin_src clojure

 (def do-all-the-things 

   (concat

    curl-script

    [[300 :d-ex 40]

     [320 :d-ex 0]]

    (shift-script 280 (take 16 wiggle-script))))

 

 (defn generate-phi-space []

   (let [experiences (atom [])]

     (run-world

      (apply-map 

       worm-world

       (merge

        (worm-world-defaults)

        {:end-frame 700

         :motor-control

         (motor-control-program worm-muscle-labels do-all-the-things)

         :experiences experiences})))

     @experiences))

    #+end_src

    #+end_listing

 

    #+caption: Use =longest-thread= and a \Phi-space generated from a short

    #+caption: exercise routine to interpret actions during free play.

    #+name: empathy-debug

 #+begin_listing clojure

    #+begin_src clojure

 (defn init []

   (def phi-space (generate-phi-space))

   (def phi-scan (gen-phi-scan phi-space)))

 

 (defn empathy-demonstration []

   (let [proprio (atom ())]

     (fn

       [experiences text]

       (let [phi-indices (phi-scan (:proprioception (peek experiences)))]

         (swap! proprio (partial cons phi-indices))

         (let [exp-thread (longest-thread (take 300 @proprio))

               empathy (mapv phi-space (infer-nils exp-thread))]

           (println-repl (vector:last-n exp-thread 22))

           (cond

            (grand-circle? empathy) (.setText text "Grand Circle")

            (curled? empathy)       (.setText text "Curled")

            (wiggling? empathy)     (.setText text "Wiggling")

            (resting? empathy)      (.setText text "Resting")

            :else                   (.setText text "Unknown")))))))

 

 (defn empathy-experiment [record]

   (.start (worm-world :experience-watch (debug-experience-phi)

                       :record record :worm worm*)))

    #+end_src

    #+end_listing

 

    These programs create a test for the empathy system. First, the

    worm's \Phi-space is generated from a simple motor script. Then the

    worm is re-created in an environment almost exactly identical to

    the testing environment for the action-predicates, with one major

    difference : the only sensory information available to the system

    is proprioception. From just the proprioception data and

    \Phi-space, =longest-thread= synthesizes a complete record the last

    300 sensory experiences of the worm. These synthesized experiences

    are fed directly into the action predicates =grand-circle?=,

    =curled?=, =wiggling?=, and =resting?= and their outputs are

    printed to the screen at each frame.

    

    The result of running =empathy-experiment= is that the system is

    generally able to interpret worm actions using the action-predicates

    on simulated sensory data just as well as with actual data. Figure

    \ref{empathy-debug-image} was generated using =empathy-experiment=:

 

   #+caption: From only proprioceptive data, =EMPATH= was able to infer 

   #+caption: the complete sensory experience and classify four poses

   #+caption: (The last panel shows a composite image of /wiggling/, 

   #+caption: a dynamic pose.)

   #+name: empathy-debug-image

   #+ATTR_LaTeX: :width 10cm :placement [H]

   [[./images/empathy-1.png]]

 

   One way to measure the performance of =EMPATH= is to compare the

   suitability of the imagined sense experience to trigger the same

   action predicates as the real sensory experience. 

   

    #+caption: Determine how closely empathy approximates actual 

    #+caption: sensory data.

    #+name: test-empathy-accuracy

 #+begin_listing clojure

    #+begin_src clojure

 (def worm-action-label

   (juxt grand-circle? curled? wiggling?))

 

 (defn compare-empathy-with-baseline [matches]

   (let [proprio (atom ())]

     (fn

       [experiences text]

       (let [phi-indices (phi-scan (:proprioception (peek experiences)))]

         (swap! proprio (partial cons phi-indices))

         (let [exp-thread (longest-thread (take 300 @proprio))

               empathy (mapv phi-space (infer-nils exp-thread))

               experience-matches-empathy

               (= (worm-action-label experiences)

                  (worm-action-label empathy))]

           (println-repl experience-matches-empathy)

           (swap! matches #(conj % experience-matches-empathy)))))))

               

 (defn accuracy [v]

   (float (/ (count (filter true? v)) (count v))))

 

 (defn test-empathy-accuracy []

   (let [res (atom [])]

     (run-world

      (worm-world :experience-watch

                  (compare-empathy-with-baseline res)

                  :worm worm*))

     (accuracy @res)))

    #+end_src

    #+end_listing

 

   Running =test-empathy-accuracy= using the very short exercise

   program =do-all-the-things= defined in listing

   \ref{generate-phi-space}, and then doing a similar pattern of

   activity using manual control of the worm, yields an accuracy of

   around 73%. This is based on very limited worm experience, and

   almost all errors are due to the worm's \Phi-space being too

   incomplete to properly interpret common poses. By manually training

   the worm for longer using =init-interactive= defined in listing

   \ref{manual-phi-space}, the accuracy dramatically improves:

 

    #+caption: Program to generate \Phi-space using manual training.

    #+name: manual-phi-space

    #+begin_listing clojure

    #+begin_src clojure

 (defn init-interactive []

   (def phi-space

     (let [experiences (atom [])]

       (run-world

        (apply-map 

         worm-world

         (merge

          (worm-world-defaults)

          {:experiences experiences})))

       @experiences))

   (def phi-scan (gen-phi-scan phi-space)))

    #+end_src

    #+end_listing

 

   =init-interactive= allows me to take direct control of the worm's

   muscles and run it through each characteristic movement I care

   about. After about 1 minute of manual training, I was able to

   achieve 95% accuracy on manual testing of the worm using

   =test-empathy-accuracy=. The majority of disagreements are near the

   transition boundaries from one type of action to another. During

   these transitions the exact label for the action is often unclear,

   and disagreement between empathy and experience is practically

   irrelevant. Thus, the system's effective identification accuracy is

   even higher than 95%. When I watch this system myself, I generally

   see no errors in action identification compared to my own judgment

   of what the worm is doing.

 

 ** Digression: Learning touch sensor layout through free play

 

    In the previous chapter I showed how to compute actions in terms of

    body-centered predicates, but some of those predicates relied on

    the average touch activation of pre-defined regions of the worm's

    skin. What if, instead of receiving touch pre-grouped into the six

    faces of each worm segment, the true partitioning of the worm's

    skin was unknown? This is more similar to how a nerve fiber bundle

    might be arranged inside an animal. While two fibers that are close

    in a nerve bundle /might/ correspond to two touch sensors that are

    close together on the skin, the process of taking a complicated

    surface and forcing it into essentially a 2D circle requires that

    some regions of skin that are close together in the animal end up

    far apart in the nerve bundle.

    

    In this chapter I show how to automatically learn the

    skin-partitioning of a worm segment by free exploration. As the

    worm rolls around on the floor, large sections of its surface get

    activated. If the worm has stopped moving, then whatever region of

    skin that is touching the floor is probably an important region,

    and should be recorded. The code I provide relies on the worm

    segment having flat faces, but still demonstrates a primitive kind

    of multi-sensory bootstrapping that I find appealing.

 

    #+caption: Program to detect whether the worm is in a resting state 

    #+caption: with one face touching the floor.

    #+name: pure-touch

    #+begin_listing clojure

    #+begin_src clojure

 (def full-contact [(float 0.0) (float 0.1)])

 

 (defn pure-touch?

   "This is worm specific code to determine if a large region of touch

    sensors is either all on or all off."

   [[coords touch :as touch-data]]

   (= (set (map first touch)) (set full-contact)))

    #+end_src

    #+end_listing

 

    After collecting these important regions, there will many nearly

    similar touch regions. While for some purposes the subtle

    differences between these regions will be important, for my

    purposes I collapse them into mostly non-overlapping sets using

    =remove-similar= in listing \ref{remove-similar}

 

    #+caption: Program to take a list of sets of points and ``collapse them''

    #+caption: so that the remaining sets in the list are significantly 

    #+caption: different from each other. Prefer smaller sets to larger ones.

    #+name: remove-similar

    #+begin_listing clojure

    #+begin_src clojure

 (defn remove-similar

   [coll]

   (loop [result () coll (sort-by (comp - count) coll)]

     (if (empty? coll) result

         (let  [[x & xs] coll

                c (count x)]

           (if (some

                (fn [other-set]

                  (let [oc (count other-set)]

                    (< (- (count (union other-set x)) c) (* oc 0.1))))

                xs)

             (recur result xs)

             (recur (cons x result) xs))))))

    #+end_src

    #+end_listing

 

    Actually running this simulation is easy given =CORTEX='s facilities.

 

    #+caption: Collect experiences while the worm moves around. Filter the touch 

    #+caption: sensations by stable ones, collapse similar ones together, 

    #+caption: and report the regions learned.

    #+name: learn-touch

    #+begin_listing clojure

    #+begin_src clojure

 (defn learn-touch-regions []

   (let [experiences (atom [])

         world (apply-map

                worm-world

                (assoc (worm-segment-defaults)

                  :experiences experiences))]

     (run-world world)

     (->>

      @experiences

      (drop 175)

      ;; access the single segment's touch data

      (map (comp first :touch))

      ;; only deal with "pure" touch data to determine surfaces

      (filter pure-touch?)

      ;; associate coordinates with touch values

      (map (partial apply zipmap))

      ;; select those regions where contact is being made

      (map (partial group-by second))

      (map #(get % full-contact))

      (map (partial map first))

      ;; remove redundant/subset regions

      (map set)

      remove-similar)))

 

 (defn learn-and-view-touch-regions []

   (map view-touch-region

        (learn-touch-regions)))

    #+end_src

    #+end_listing

 

    The only thing remaining to define is the particular motion the worm

    must take. I accomplish this with a simple motor control program.

 

    #+caption: Motor control program for making the worm roll on the ground.

    #+caption: This could also be replaced with random motion.

    #+name: worm-roll

    #+begin_listing clojure

    #+begin_src clojure

 (defn touch-kinesthetics []

   [[170 :lift-1 40]

    [190 :lift-1 19]

    [206 :lift-1  0]

 

    [400 :lift-2 40]

    [410 :lift-2  0]

 

    [570 :lift-2 40]

    [590 :lift-2 21]

    [606 :lift-2  0]

 

    [800 :lift-1 30]

    [809 :lift-1 0]

 

    [900 :roll-2 40]

    [905 :roll-2 20]

    [910 :roll-2  0]

 

    [1000 :roll-2 40]

    [1005 :roll-2 20]

    [1010 :roll-2  0]

    

    [1100 :roll-2 40]

    [1105 :roll-2 20]

    [1110 :roll-2  0]

    ])

    #+end_src

    #+end_listing

 

 

    #+caption: The small worm rolls around on the floor, driven

    #+caption: by the motor control program in listing \ref{worm-roll}.

    #+name: worm-roll

    #+ATTR_LaTeX: :width 12cm

    [[./images/worm-roll.png]]

 

    #+caption: After completing its adventures, the worm now knows 

    #+caption: how its touch sensors are arranged along its skin. Each of these six rectangles are touch sensory patterns that were 

    #+caption: deemed important by 

    #+caption: =learn-touch-regions=. Each white square in the rectangles 

    #+caption: above is a cluster of ``related" touch nodes as determined 

    #+caption: by the system. The worm has correctly discovered that it has six faces, and has partitioned its sensory map into these six faces. 

    #+name: worm-touch-map

    #+ATTR_LaTeX: :width 12cm

    [[./images/touch-learn.png]]

 

    While simple, =learn-touch-regions= exploits regularities in both

    the worm's physiology and the worm's environment to correctly

    deduce that the worm has six sides. Note that =learn-touch-regions=

    would work just as well even if the worm's touch sense data were

    completely scrambled. The cross shape is just for convenience. This

    example justifies the use of pre-defined touch regions in =EMPATH=.

 

 ** Recognizing an object using embodied representation

 

    At the beginning of the thesis, I suggested that we might recognize

    the chair in Figure \ref{hidden-chair} by imagining ourselves in

    the position of the man and realizing that he must be sitting on

    something in order to maintain that position. Here, I present a

    brief elaboration on how to this might be done.

 

    First, I need the feeling of leaning or resting /on/ some other

    object that is not the floor. This feeling is easy to describe

    using an embodied representation. 

 

   #+caption: Program describing the sense of leaning or resting on something.

   #+caption: This involves a relaxed posture, the feeling of touching something, 

   #+caption: and a period of stability where the worm does not move.

   #+name: draped

   #+begin_listing clojure

   #+begin_src clojure

 (defn draped?

   "Is the worm:

     -- not flat (the floor is not a 'chair')

     -- supported (not using its muscles to hold its position)

     -- stable (not changing its position)

     -- touching something (must register contact)"

   [experiences]

   (let [b2-hash (bin 2)

         touch (:touch (peek experiences))

         total-contact

         (reduce

          +

          (map #(contact all-touch-coordinates %)

               (rest touch)))]

     (println total-contact)

     (and (not (resting? experiences))

          (every?

           zero?

           (-> experiences

               (vector:last-n 25)

               (#(map :muscle %))

               (flatten)))

          (-> experiences

              (vector:last-n 20)

              (#(map (comp b2-hash flatten :proprioception) %))

              (set)

              (count) (= 1))

          (< 0.03 total-contact))))

   #+end_src

   #+end_listing

    

    #+caption: The =draped?= predicate detects the presence of the cube

    #+caption: whenever the worm interacts with it. The details of the

    #+caption: cube are irrelevant; only the way it influences the

    #+caption: worm's body matters. The ``unknown'' label on the fifth

    #+caption: frame is due to the fact that the worm is not

    #+caption: stationary. =draped?= will only declare that the worm

    #+caption: is draped if it has been still for a while.

    #+name: draped-video

    #+ATTR_LaTeX: :width 13cm

    [[./images/draped.png]]

 

    Though this is a simple example, using the =draped?= predicate to

    detect a cube has interesting advantages. The =draped?= predicate

    describes the cube not in terms of properties that the cube has,

    but instead in terms of how the worm interacts with it physically.

    This means that the cube can still be detected even if it is not

    visible, as long as its influence on the worm's body is visible.

    

    This system will also see the virtual cube created by a

    ``mimeworm", which uses its muscles in a very controlled way to

    mimic the appearance of leaning on a cube. The system will

    anticipate that there is an actual invisible cube that provides

    support!

 

    #+caption: Can you see the thing that this person is leaning on?

    #+caption: What properties does it have, other than how it makes

    #+caption: the man's elbow and shoulder feel? I wonder if people

    #+caption: who can actually maintain this pose easily still see the

    #+caption: support?

    #+name: mime

    #+ATTR_LaTeX: :width 6cm

    [[./images/pablo-the-mime.png]]

 

    This makes me wonder about the psychology of actual mimes. Suppose

    for a moment that people have something analogous to \Phi-space and

    that one of the ways that they find objects in a scene is by their

    relation to other people's bodies. Suppose that a person watches a

    person miming an invisible wall. For a person with no experience

    with miming, their \Phi-space will only have entries that describe

    the scene with the sensation of their hands touching a wall. This

    sensation of touch will create a strong impression of a wall, even

    though the wall would have to be invisible. A person with

    experience in miming however, will have entries in their \Phi-space

    that describe the wall-miming position without a sense of touch. It

    will not seem to such as person that an invisible wall is present,

    but merely that the mime is holding out their hands in a special

    way. Thus, the theory that humans use something like \Phi-space

    weakly predicts that learning how to mime should break the power of

    miming illusions. Most optical illusions still work no matter how

    much you know about them, so this proposal would be quite

    interesting to test, as it predicts a non-standard result!

 

 

 #+BEGIN_LaTeX

 \clearpage

 #+END_LaTeX

 

 * Contributions

 

   The big idea behind this thesis is a new way to represent and

   recognize physical actions, which I call /empathic representation/.

   Actions are represented as predicates which have access to the

   totality of a creature's sensory abilities. To recognize the

   physical actions of another creature similar to yourself, you

   imagine what they would feel by examining the position of their body

   and relating it to your own previous experience.

   

   Empathic representation of physical actions is robust and general.

   Because the representation is body-centered, it avoids baking in a

   particular viewpoint like you might get from learning from example

   videos. Because empathic representation relies on all of a

   creature's senses, it can describe exactly what an action /feels

   like/ without getting caught up in irrelevant details such as visual

   appearance. I think it is important that a correct description of

   jumping (for example) should not include irrelevant details such as

   the color of a person's clothes or skin; empathic representation can

   get right to the heart of what jumping is by describing it in terms

   of touch, muscle contractions, and a brief feeling of

   weightlessness. Empathic representation is very low-level in that it

   describes actions using concrete sensory data with little

   abstraction, but it has the generality of much more abstract

   representations!

 

   Another important contribution of this thesis is the development of

   the =CORTEX= system, a complete environment for creating simulated

   creatures. You have seen how to implement five senses: touch,

   proprioception, hearing, vision, and muscle tension. You have seen

   how to create new creatures using Blender, a 3D modeling tool.

 

   As a minor digression, you also saw how I used =CORTEX= to enable a

   tiny worm to discover the topology of its skin simply by rolling on

   the ground.  You also saw how to detect objects using only embodied

   predicates. 

 

   In conclusion, for this thesis I:

 

    - Developed the idea of embodied representation, which describes

      actions that a creature can do in terms of first-person sensory

      data. 

 

    - Developed a method of empathic action recognition which uses

      previous embodied experience and embodied representation of

      actions to greatly constrain the possible interpretations of an

      action.

 

    - Created =EMPATH=, a program which uses empathic action

      recognition to recognize physical actions in a simple model

      involving segmented worm-like creatures.

 

    - Created =CORTEX=, a comprehensive platform for embodied AI

      experiments. It is the base on which =EMPATH= is built.

 

 #+BEGIN_LaTeX

 \clearpage

 \appendix

 #+END_LaTeX

 

 * Appendix: =CORTEX= User Guide

 

   Those who write a thesis should endeavor to make their code not only

   accessible, but actually usable, as a way to pay back the community

   that made the thesis possible in the first place. This thesis would

   not be possible without Free Software such as jMonkeyEngine3,

   Blender, clojure, =emacs=, =ffmpeg=, and many other tools. That is

   why I have included this user guide, in the hope that someone else

   might find =CORTEX= useful.

 

 ** Obtaining =CORTEX= 

 

    You can get cortex from its mercurial repository at

    http://hg.bortreb.com/cortex. You may also download =CORTEX=

    releases at http://aurellem.org/cortex/releases/. As a condition of

    making this thesis, I have also provided Professor Winston the

    =CORTEX= source, and he knows how to run the demos and get started.

    You may also email me at =cortex@aurellem.org= and I may help where

    I can.

 

 ** Running =CORTEX= 

    

    =CORTEX= comes with README and INSTALL files that will guide you

    through installation and running the test suite. In particular you

    should look at test =cortex.test= which contains test suites that

    run through all senses and multiple creatures.

 

 ** Creating creatures

 

    Creatures are created using /Blender/, a free 3D modeling program.

    You will need Blender version 2.6 when using the =CORTEX= included

    in this thesis. You create a =CORTEX= creature in a similar manner

    to modeling anything in Blender, except that you also create

    several trees of empty nodes which define the creature's senses.

 

 *** Mass 

     

     To give an object mass in =CORTEX=, add a ``mass'' metadata label

     to the object with the mass in jMonkeyEngine units. Note that

     setting the mass to 0 causes the object to be immovable.

 

 *** Joints

 

     Joints are created by creating an empty node named =joints= and

     then creating any number of empty child nodes to represent your

     creature's joints. The joint will automatically connect the

     closest two physical objects. It will help to set the empty node's

     display mode to ``Arrows'' so that you can clearly see the

     direction of the axes.

    

     Joint nodes should have the following metadata under the ``joint''

     label:

 

     #+BEGIN_SRC clojure

 ;; ONE of the following, under the label "joint":

 {:type :point}

 

 ;; OR

 

 {:type :hinge

  :limit [<limit-low> <limit-high>]

  :axis (Vector3f. <x> <y> <z>)}

 ;;(:axis defaults to (Vector3f. 1 0 0) if not provided for hinge joints)

 

 ;; OR

 

 {:type :cone

  :limit-xz <lim-xz>

  :limit-xy <lim-xy>

  :twist    <lim-twist>}   ;(use XZY rotation mode in Blender!)

     #+END_SRC

 

 *** Eyes

 

     Eyes are created by creating an empty node named =eyes= and then

     creating any number of empty child nodes to represent your

     creature's eyes.

 

     Eye nodes should have the following metadata under the ``eye''

     label:

 

 #+BEGIN_SRC clojure

 {:red    <red-retina-definition>

  :blue   <blue-retina-definition>

  :green  <green-retina-definition>

  :all    <all-retina-definition>

  (<0xrrggbb> <custom-retina-image>)...

 }

 #+END_SRC

 

     Any of the color channels may be omitted. You may also include

     your own color selectors, and in fact :red is equivalent to

     0xFF0000 and so forth. The eye will be placed at the same position

     as the empty node and will bind to the neatest physical object.

     The eye will point outward from the X-axis of the node, and ``up''

     will be in the direction of the X-axis of the node. It will help

     to set the empty node's display mode to ``Arrows'' so that you can

     clearly see the direction of the axes.

 

     Each retina file should contain white pixels wherever you want to be

     sensitive to your chosen color. If you want the entire field of

     view, specify :all of 0xFFFFFF and a retinal map that is entirely

     white. 

 

     Here is a sample retinal map:

 

     #+caption: An example retinal profile image. White pixels are 

     #+caption: photo-sensitive elements. The distribution of white 

     #+caption: pixels is denser in the middle and falls off at the 

     #+caption: edges and is inspired by the human retina.

     #+name: retina

     #+ATTR_LaTeX: :width 7cm :placement [H]

     [[./images/retina-small.png]]

 

 *** Hearing

 

     Ears are created by creating an empty node named =ears= and then

     creating any number of empty child nodes to represent your

     creature's ears. 

 

     Ear nodes do not require any metadata.

 

     The ear will bind to and follow the closest physical node.

 

 *** Touch

 

     Touch is handled similarly to mass. To make a particular object

     touch sensitive, add metadata of the following form under the

     object's ``touch'' metadata field:

     

     #+BEGIN_EXAMPLE

     <touch-UV-map-file-name>    

     #+END_EXAMPLE

 

     You may also include an optional ``scale'' metadata number to

     specify the length of the touch feelers. The default is $0.1$,

     and this is generally sufficient.

 

     The touch UV should contain white pixels for each touch sensor.

 

     Here is an example touch-uv map that approximates a human finger,

     and its corresponding model.

 

     #+caption: This is the tactile-sensor-profile for the upper segment 

     #+caption: of a fingertip. It defines regions of high touch sensitivity 

     #+caption: (where there are many white pixels) and regions of low 

     #+caption: sensitivity (where white pixels are sparse).

     #+name: guide-fingertip-UV

     #+ATTR_LaTeX: :width 9cm :placement [H]

     [[./images/finger-UV.png]]

 

     #+caption: The fingertip UV-image form above applied to a simple

     #+caption: model of a fingertip.

     #+name: guide-fingertip

     #+ATTR_LaTeX: :width 9cm :placement [H]

     [[./images/finger-1.png]]

 

 *** Proprioception

 

     Proprioception is tied to each joint node -- nothing special must

     be done in a Blender model to enable proprioception other than

     creating joint nodes.

 

 *** Muscles

 

     Muscles are created by creating an empty node named =muscles= and

     then creating any number of empty child nodes to represent your

     creature's muscles.

 

     

     Muscle nodes should have the following metadata under the

     ``muscle'' label:

 

     #+BEGIN_EXAMPLE

     <muscle-profile-file-name>

     #+END_EXAMPLE

 

     Muscles should also have a ``strength'' metadata entry describing

     the muscle's total strength at full activation. 

 

     Muscle profiles are simple images that contain the relative amount

     of muscle power in each simulated alpha motor neuron. The width of

     the image is the total size of the motor pool, and the redness of

     each neuron is the relative power of that motor pool.

 

     While the profile image can have any dimensions, only the first

     line of pixels is used to define the muscle. Here is a sample

     muscle profile image that defines a human-like muscle.

 

     #+caption: A muscle profile image that describes the strengths

     #+caption: of each motor neuron in a muscle. White is weakest 

     #+caption: and dark red is strongest. This particular pattern 

     #+caption: has weaker motor neurons at the beginning, just 

     #+caption: like human muscle.

     #+name: muscle-recruit

     #+ATTR_LaTeX: :width 7cm :placement [H]

     [[./images/basic-muscle.png]]

     

     Muscles twist the nearest physical object about the muscle node's

     Z-axis. I recommend using the ``Single Arrow'' display mode for

     muscles and using the right hand rule to determine which way the

     muscle will twist. To make a segment that can twist in multiple

     directions, create multiple, differently aligned muscles.

 

 ** =CORTEX= API

 

    These are the some functions exposed by =CORTEX= for creating

    worlds and simulating creatures. These are in addition to

    jMonkeyEngine3's extensive library, which is documented elsewhere.

 

 *** Simulation

    - =(world root-node key-map setup-fn update-fn)= :: create

         a simulation.

      - /root-node/     :: a =com.jme3.scene.Node= object which

           contains all of the objects that should be in the

           simulation.

 

      - /key-map/       :: a map from strings describing keys to

           functions that should be executed whenever that key is

           pressed. the functions should take a SimpleApplication

           object and a boolean value. The SimpleApplication is the

           current simulation that is running, and the boolean is true

           if the key is being pressed, and false if it is being

           released. As an example,

           #+BEGIN_SRC clojure

        {"key-j" (fn [game value] (if value (println "key j pressed")))}		  

 	  #+END_SRC

 	  is a valid key-map which will cause the simulation to print

           a message whenever the 'j' key on the keyboard is pressed.

 

      - /setup-fn/      :: a function that takes a =SimpleApplication=

           object. It is called once when initializing the simulation.

           Use it to create things like lights, change the gravity,

           initialize debug nodes, etc.

 

      - /update-fn/     :: this function takes a =SimpleApplication=

           object and a float and is called every frame of the

           simulation. The float tells how many seconds is has been

           since the last frame was rendered, according to whatever

           clock jme is currently using. The default is to use IsoTimer

           which will result in this value always being the same.

 

    - =(position-camera world position rotation)= :: set the position

         of the simulation's main camera.

 

    - =(enable-debug world)= :: turn on debug wireframes for each

         simulated object.

 

    - =(set-gravity world gravity)= :: set the gravity of a running

         simulation.

 

    - =(box length width height & {options})= :: create a box in the

         simulation. Options is a hash map specifying texture, mass,

         etc. Possible options are =:name=, =:color=, =:mass=,

         =:friction=, =:texture=, =:material=, =:position=,

         =:rotation=, =:shape=, and =:physical?=.

 

    - =(sphere radius & {options})= :: create a sphere in the simulation.

         Options are the same as in =box=.

 

    - =(load-blender-model file-name)= :: create a node structure

         representing the model described in a Blender file.

 

    - =(light-up-everything world)= :: distribute a standard compliment

         of lights throughout the simulation. Should be adequate for most

         purposes.

 

    - =(node-seq node)= :: return a recursive list of the node's

         children.

 

    - =(nodify name children)= :: construct a node given a node-name and

         desired children.

 

    - =(add-element world element)= :: add an object to a running world

         simulation.

 

    - =(set-accuracy world accuracy)= :: change the accuracy of the

         world's physics simulator.

 

    - =(asset-manager)= :: get an /AssetManager/, a jMonkeyEngine

         construct that is useful for loading textures and is required

         for smooth interaction with jMonkeyEngine library functions.

 

    - =(load-bullet)=   :: unpack native libraries and initialize the

         bullet physics subsystem. This function is required before

         other world building functions are called.

 	

 *** Creature Manipulation / Import

 

    - =(body! creature)= :: give the creature a physical body.

 

    - =(vision! creature)= :: give the creature a sense of vision.

         Returns a list of functions which will each, when called

         during a simulation, return the vision data for the channel of

         one of the eyes. The functions are ordered depending on the

         alphabetical order of the names of the eye nodes in the

         Blender file. The data returned by the functions is a vector

         containing the eye's /topology/, a vector of coordinates, and

         the eye's /data/, a vector of RGB values filtered by the eye's

         sensitivity. 

 

    - =(hearing! creature)= :: give the creature a sense of hearing.

         Returns a list of functions, one for each ear, that when

         called will return a frame's worth of hearing data for that

         ear. The functions are ordered depending on the alphabetical

         order of the names of the ear nodes in the Blender file. The

         data returned by the functions is an array of PCM (pulse code

         modulated) wav data.

 

    - =(touch! creature)= :: give the creature a sense of touch. Returns

         a single function that must be called with the /root node/ of

         the world, and which will return a vector of /touch-data/

         one entry for each touch sensitive component, each entry of

         which contains a /topology/ that specifies the distribution of

         touch sensors, and the /data/, which is a vector of

         =[activation, length]= pairs for each touch hair.

 

    - =(proprioception! creature)= :: give the creature the sense of

         proprioception. Returns a list of functions, one for each

         joint, that when called during a running simulation will

         report the =[heading, pitch, roll]= of the joint.

 

    - =(movement! creature)= :: give the creature the power of movement.

         Creates a list of functions, one for each muscle, that when

         called with an integer, will set the recruitment of that

         muscle to that integer, and will report the current power

         being exerted by the muscle. Order of muscles is determined by

         the alphabetical sort order of the names of the muscle nodes.

 	

 *** Visualization/Debug

 

    - =(view-vision)= :: create a function that when called with a list

         of visual data returned from the functions made by =vision!=, 

         will display that visual data on the screen.

 

    - =(view-hearing)= :: same as =view-vision= but for hearing.

 

    - =(view-touch)= :: same as =view-vision= but for touch.

 

    - =(view-proprioception)= :: same as =view-vision= but for

         proprioception.

 

    - =(view-movement)= :: same as =view-vision= but for muscles.

 

    - =(view anything)= :: =view= is a polymorphic function that allows

         you to inspect almost anything you could reasonably expect to

         be able to ``see'' in =CORTEX=.

 

    - =(text anything)= :: =text= is a polymorphic function that allows

         you to convert practically anything into a text string.	

 

    - =(println-repl anything)= :: print messages to clojure's repl

         instead of the simulation's terminal window.

 

    - =(mega-import-jme3)= :: for experimenting at the REPL. This

         function will import all jMonkeyEngine3 classes for immediate

         use.

 

    - =(display-dilated-time world timer)= :: Shows the time as it is

         flowing in the simulation on a HUD display.