Here I explore the design and capabilities of my system (called

 =CORTEX=) which enables experiments in /embodied artificial

 intelligence/ -- that is, AI which uses a physical simulation of

 reality accompanied by a simulated body to solve problems.

 

 In the first half of the thesis I describe the construction of

 =CORTEX= and the rationale behind my architecture choices. =CORTEX= is

 a complete platform for embodied AI research. It provides multiple

 senses for simulated creatures, including vision, touch,

 proprioception, muscle tension, and hearing. Each of these senses

 provides a wealth of parameters that are biologically

 inspired. =CORTEX= is able to simulate any number of creatures and

 senses, and provides facilities for easily modeling and creating new

 creatures. As a research platform it is more complete than any other

 system currently available.

 

 In the second half of the thesis I develop a computational model of

 empathy, using =CORTEX= as a base. Empathy in this context is the

 ability to observe another creature and infer what sorts of sensations

 that creature is feeling. My empathy algorithm involves multiple

 phases. First is free-play, where the creature moves around and gains

 sensory experience. From this experience I construct a representation

 of the creature's sensory state space, which I call \phi-space. Using

 \phi-space, I construct an efficient function for enriching the

 limited data that comes from observing another creature with a full

 compliment of imagined sensory data based on previous experience. I

 can then use the imagined sensory data to recognize what the observed

 creature is doing and feeling, using straightforward embodied action

 predicates. This is all demonstrated with using a simple worm-like

 creature, recognizing worm-actions in video.