Mercurial > cortex
view 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 Here I demonstrate the power of using embodied artificial intelligence2 to attack the /action recognition/ problem, which is the challenge of3 recognizing actions performed by a creature given limited data about4 the creature's actions, such as a video recording. I solve this problem5 in the case of a worm-like creature performing actions such as curling6 and wiggling.8 To attack the action recognition problem, I developed a computational9 model of empathy which allows me to use simple, embodied10 representations of actions (which require rich sensory data), even11 when that sensory data is not actually available. The missing sense12 data is ``imagined'' by the system by combining previous experiences13 gained from unsupervised free play.15 In order to build this empathic, action-recognizing system, I created16 a program called =CORTEX=, which is a complete platform for embodied17 AI research. It provides multiple senses for simulated creatures,18 including vision, touch, proprioception, muscle tension, and19 hearing. Each of these senses provides a wealth of parameters that are20 biologically inspired. =CORTEX= is able to simulate any number of21 creatures and senses, and provides facilities for easily modeling and22 creating new creatures. As a research platform it is more complete23 than any other system currently available.