Mercurial > cortex
diff thesis/abstract.org @ 436:853377051f1e
abstract v. 2
author | Robert McIntyre <rlm@mit.edu> |
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date | Sun, 23 Mar 2014 19:09:14 -0400 |
parents | ae3bfc82ac7c |
children | c1e6b7221b2f |
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1.1 --- a/thesis/abstract.org Sun Mar 23 17:59:16 2014 -0400 1.2 +++ b/thesis/abstract.org Sun Mar 23 19:09:14 2014 -0400 1.3 @@ -1,34 +1,23 @@ 1.4 -Here I explore the design and capabilities of my system (called 1.5 -=CORTEX=) which enables experiments in /embodied artificial 1.6 -intelligence/ -- that is, AI which uses a physical simulation of 1.7 -reality accompanied by a simulated body to solve problems. 1.8 +Here I demonstrate the power of using embodied artificial intelligence 1.9 +to attack the /action recognition/ problem, which is the challenge of 1.10 +recognizing actions performed by a creature given limited data about 1.11 +the creature's actions, such as a video recording. I solve this problem 1.12 +in the case of a worm-like creature performing actions such as curling 1.13 +and wiggling. 1.14 1.15 -In the first half of the thesis I describe the construction of 1.16 -=CORTEX= and the rationale behind my architecture choices. =CORTEX= is 1.17 -a complete platform for embodied AI research. It provides multiple 1.18 -senses for simulated creatures, including vision, touch, 1.19 -proprioception, muscle tension, and hearing. Each of these senses 1.20 -provides a wealth of parameters that are biologically 1.21 -inspired. =CORTEX= is able to simulate any number of creatures and 1.22 -senses, and provides facilities for easily modeling and creating new 1.23 -creatures. As a research platform it is more complete than any other 1.24 -system currently available. 1.25 +To attack the action recognition problem, I developed a computational 1.26 +model of empathy which allows me to use simple, embodied 1.27 +representations of actions (which require rich sensory data), even 1.28 +when that sensory data is not actually available. The missing sense 1.29 +data is ``imagined'' by the system by combining previous experiences 1.30 +gained from unsupervised free play. 1.31 1.32 -In the second half of the thesis I develop a computational model of 1.33 -empathy, using =CORTEX= as a base. Empathy in this context is the 1.34 -ability to observe another creature and infer what sorts of sensations 1.35 -that creature is feeling. My empathy algorithm involves multiple 1.36 -phases. First is free-play, where the creature moves around and gains 1.37 -sensory experience. From this experience I construct a representation 1.38 -of the creature's sensory state space, which I call \phi-space. Using 1.39 -\phi-space, I construct an efficient function for enriching the 1.40 -limited data that comes from observing another creature with a full 1.41 -compliment of imagined sensory data based on previous experience. I 1.42 -can then use the imagined sensory data to recognize what the observed 1.43 -creature is doing and feeling, using straightforward embodied action 1.44 -predicates. This is all demonstrated with using a simple worm-like 1.45 -creature, and recognizing worm-actions in video. 1.46 - 1.47 - 1.48 -empathy is important 1.49 -cortex tests that idea 1.50 \ No newline at end of file 1.51 +In order to build this empathic, action-recognizing system, I created 1.52 +a program called =CORTEX=, which is a complete platform for embodied 1.53 +AI research. It provides multiple senses for simulated creatures, 1.54 +including vision, touch, proprioception, muscle tension, and 1.55 +hearing. Each of these senses provides a wealth of parameters that are 1.56 +biologically inspired. =CORTEX= is able to simulate any number of 1.57 +creatures and senses, and provides facilities for easily modeling and 1.58 +creating new creatures. As a research platform it is more complete 1.59 +than any other system currently available.