Learning Plans without a priori Knowledge  

Oleh:

Sun, R. ; Sessions, Chad

Jenis: Article from Journal - e-Journal
Dalam koleksi: Adaptive Behavior vol. 8 no. 3-4 (Jun. 2000), page 225-254.
Topik: Learning plans; domain-specific knowledge; priori knowledge
Fulltext: 225.pdf (2.2MB)

Isi artikel

This paper is concerned with the autonomous learning of plans in probabilistic domains without 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 substantial 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 priori 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.

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