from subsymbolic to symbolic learning

[Ron Sun]

Bottom-up Skill Learning in Reactive Sequential Decision Tasks

Ron Sun 
Todd Peterson 
Edward Merrill

The University of Alabama
Tuscaloosa, AL 35487

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To appear in: Proc. of Cognitive Science Conference, 1996.
6 pages.

ftp or Web access:
ftp://cs.ua.edu/pub/tech-reports/sun.cogsci96.ps

sorry, no hardcopy available.
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This paper introduces a hybrid model that unifies
connectionist, symbolic, and reinforcement learning into  
an integrated architecture for bottom-up skill learning 
in reactive sequential decision tasks.
The model is designed for an agent to learn continuously
from on-going experience in the world, without the use of
preconceived concepts and knowledge.  Both procedural 
skills and high-level knowledge are acquired through an
agent's experience interacting with the world. Computational 
experiments with the model in two domains are reported.