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Published in:
2007 | OriginalPaper | Chapter
HPGP: An Abstraction-Based Framework for Decision-Theoretic Planning
Authors : Letícia Friske, Carlos Henrique Costa Ribeiro
Published in: Intelligent Data Engineering and Automated Learning - IDEAL 2007
Publisher: Springer Berlin Heidelberg
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This paper is a report on research towards the development of an abstraction-based framework for decision-theoretic planning. We make use of two planning approaches in the context of probabilistic planning: planning by abstraction and planning graphs. To create abstraction hierarchies our planner uses an adapted version of a hierarchical planner under uncertainty, and to search for plans, we propose a probabilistic planning algorithm based on Pgraphplan. The article outlines the main framework characteristics, and presents results on some problems found in the literature. Our preliminary results suggest that our planner can reduce the size of the search space, when compared with Pgraphplan, hierarchical planning under uncertainty and top-down dynamic programming.