Subject: TR available

From: Jiajie Zhang <[email-masked]>
Date: Fri, 25 Mar 94 15:43:27 EST

Bottom-Up Recognition Learning: A Compilation-Based Model of
			Limited-Lookahead  Learning

				Todd T. Johnson
			Division of Medical Informatics
			   The Ohio State University

			Jiajie Zhang & Hongbin Wang
			Department of Psychology
			The Ohio State University

(Paper presented at the 13th SOAR workshop, also submitted to The 16th
Annual Conference of the Cognitive Science Society.)

                                ABSTRACT
When faced with a novel problem, people can sometimes decide what to
do by imagining alternative sequences of actions and then taking the
sequence that solves the problem. In many problems, however, various
constraints, such as working memory capacity, limit the amount of
lookahead that people can do. The chunking-learning hypothesis says
that all long term learning occurs through chunking, a form of
knowledge compilation; however, limited-lookahead learning has not
previously been modeled using chunking. This paper describes Bottom-Up
Recognition Learning (BURL), a model of limited-lookahead learning
based on chunking. In BURL, chunking over successive problem-solving
trials transfers knowledge from the leaf nodes of a problem space to
the top node. Two experiments test BURL's predictions. The first
compares the Soar implementation of BURL to human subjects learning to
play two Tic-Tac-Toe isomorphs. This experiment shows that BURL can
account for learning that occurs when subjects can perform a limited
lookahead. The second experiment studies transfer between two strategy
acquisition tasks for one isomorph. This experiment shows that BURL
must be used in conjunction with other learning methods to fully
explain skill acquisition on limited-lookahead tasks.

Available via anonymous ftp from 

canyon.psy.ohio-state.edu (128.146.112.91) 

pub/papers/BURL.ps.Z

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