Using domain-configurable search control for probabilistic planning

Authors:
Ugur Kuter;Dana Nau
Affiliations:
Department of Computer Science and Institute for Systems Research, University of Maryland, College Park, MD;Department of Computer Science and Institute for Systems Research, University of Maryland, College Park, MD
Venue:
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Year:
2005

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Abstract

We describe how to improve the performance of MDP planning algorithms by modifying them to use the search-control mechanisms of planners such as TLPlan, SHOP2, and TALplanner. In our experiments, modified versions of RTDP, LRTDP, and Value Iteration were exponentially faster than the original algorithms. On the largest problems the original algorithms could solve, the modified ones were about 10,000 times faster. On another set. of problems whose state spaces were more than 14,000 times larger than the original algorithms could solve, the modified algorithms took only about 1/3 second.