cortex: thesis/cortex.org comparison

comparison thesis/cortex.org @ 531:749452f063e5

elaborate paragraph.

author	Robert McIntyre <rlm@mit.edu>
date	Sat, 26 Apr 2014 20:10:20 -0400
parents	21b8389922ee
children	686f77b88292

comparison

equal deleted inserted replaced

-:21b8389922ee
+:749452f063e5
 (movement-kernel creature muscle)))
 #+END_SRC
 #+end_listing
-=movement-kernel= creates a function that controlls the movement
+=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
 :proprioception (proprioception! model)
 :muscles (movement! model)}))
 #+end_src
 #+end_listing
-** COMMENT Embodiment factors action recognition into manageable parts
+** 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
 [[./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 frame
+confused in visual details of the worm. Each one is independent of
-independent, but more than that, they are each independent of
+position and rotation, but more than that, they are each
-irrelevant visual details of the worm and the environment. They
+independent of irrelevant visual details of the worm and the
-will work regardless of whether the worm is a different color or
+environment. They will work regardless of whether the worm is a
-heavily textured, or if the environment has strange lighting.
+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. If I can do that, then
 I will have gained much.
-** COMMENT \Phi-space describes the worm's experiences
+** \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.
 There is a simple way of taking \Phi-space and the total ordering
 provided by an experience vector and reliably inferring the rest of
 the senses.
-** COMMENT Empathy is the process of tracing though \Phi-space
+** Empathy is the process of tracing though \Phi-space
 Here is the core of a basic empathy algorithm, starting with an
 experience vector:
 First, group the experiences into tiered proprioceptive bins. I use
 #+end_listing
 #+caption: =longest-thread= finds the longest path of consecutive
 #+caption: experiences to explain proprioceptive worm data from
 #+caption: previous data. Here, the film strip represents the
-#+caption: creature's previous experience. Sort sequeuces of
+#+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
 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=.
-* COMMENT Contributions
+* Contributions
 In this thesis you have seen 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

Mercurial > cortex

comparison thesis/cortex.org @ 531:749452f063e5