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Learning Plans without a priori Knowledge

CECS Department, University of Missouri

Chad Sessions

University of Alabama

This paper is concerned with the autonomous learning of plans in probabilistic domains with out a priori domain-specific knowledge. In contrast to existing reinforcement learning algorithms that generate only reactive plans, and existing probabilistic planning algorithms that require a sub stantial amount of a priori knowledge in order to plan, a two-stage bottom-up process is devised in which first reinforcement learning/dynamic programming is applied, without the use of a pri ori domain-specific knowledge, to acquire a reactive plan, and then explicit plans are extracted from the reactive plan. Several options for plan extraction are examined, each of which is based on a beam search that performs temporal projection in a restricted fashion, guided by the value functions resulting from reinforcement learning/dynamic programming. Some completeness and soundness results are given. Examples in several domains are discussed that together demonstrate the working of the proposed model.

Adaptive Behavior, Vol. 8, No. 3-4, 225-253 (2000)
DOI: 10.1177/105971230000800302


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