view thesis/abstract.org @ 469:ae10f35022ba

completed first draft of body section.
author Robert McIntyre <rlm@mit.edu>
date Fri, 28 Mar 2014 16:34:35 -0400
parents c20de2267d39
children 8e52a2802821
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1 Here I demonstrate the power of using embodied artificial intelligence
2 to attack the /action recognition/ problem, which is the challenge of
3 recognizing actions performed by a creature given limited data about
4 the creature's actions, such as a video recording. I solve this
5 problem in the case of a worm-like creature performing actions such as
6 curling and wiggling.
8 To attack the action recognition problem, I developed a computational
9 model of empathy (=EMPATH=) which allows me to recognize actions using
10 simple, embodied representations of actions (which require rich
11 sensory data), even when that sensory data is not actually
12 available. The missing sense data is ``imagined'' by the system by
13 combining previous experiences gained from unsupervised free play.
15 In order to build this empathic, action-recognizing system, I created
16 a program called =CORTEX=, which is a complete platform for embodied
17 AI research. It provides multiple senses for simulated creatures,
18 including vision, touch, proprioception, muscle tension, and
19 hearing. Each of these senses provides a wealth of parameters that are
20 biologically inspired. =CORTEX= is able to simulate any number of
21 creatures and senses, and provides facilities for easily modeling and
22 creating new creatures. As a research platform it is more complete
23 than any other system currently available.