annotate thesis/abstract.org @ 440:b01c070b03d4

save for tonight.
author Robert McIntyre <rlm@mit.edu>
date Sun, 23 Mar 2014 23:43:20 -0400
parents c1e6b7221b2f
children c20de2267d39
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@437 9 model of empathy (=EMPATH=) which allows me to use simple, embodied
rlm@436 10 representations of actions (which require rich sensory data), even
rlm@436 11 when that sensory data is not actually available. The missing sense
rlm@436 12 data is ``imagined'' by the system by combining previous experiences
rlm@436 13 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.