annotate thesis/abstract.org @ 474:57c7d5aec8d5

mix in touch; need to clean it up.
author Robert McIntyre <rlm@mit.edu>
date Fri, 28 Mar 2014 21:05:12 -0400
parents c20de2267d39
children 8e52a2802821
rev   line source
rlm@436 1 Here I demonstrate the power of using embodied artificial intelligence
rlm@436 2 to attack the /action recognition/ problem, which is the challenge of
rlm@436 3 recognizing actions performed by a creature given limited data about
rlm@437 4 the creature's actions, such as a video recording. I solve this
rlm@437 5 problem in the case of a worm-like creature performing actions such as
rlm@437 6 curling and wiggling.
rlm@432 7
rlm@436 8 To attack the action recognition problem, I developed a computational
rlm@441 9 model of empathy (=EMPATH=) which allows me to recognize actions using
rlm@441 10 simple, embodied representations of actions (which require rich
rlm@441 11 sensory data), even when that sensory data is not actually
rlm@441 12 available. The missing sense data is ``imagined'' by the system by
rlm@441 13 combining previous experiences gained from unsupervised free play.
rlm@432 14
rlm@436 15 In order to build this empathic, action-recognizing system, I created
rlm@436 16 a program called =CORTEX=, which is a complete platform for embodied
rlm@436 17 AI research. It provides multiple senses for simulated creatures,
rlm@436 18 including vision, touch, proprioception, muscle tension, and
rlm@436 19 hearing. Each of these senses provides a wealth of parameters that are
rlm@436 20 biologically inspired. =CORTEX= is able to simulate any number of
rlm@436 21 creatures and senses, and provides facilities for easily modeling and
rlm@436 22 creating new creatures. As a research platform it is more complete
rlm@436 23 than any other system currently available.