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Conference Paper

Fast Probabilistic Planning Through Weighted Model Counting


Hoffmann,  Jörg
Programming Logics, MPI for Informatics, Max Planck Society;

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Domshlak, C., & Hoffmann, J. (2006). Fast Probabilistic Planning Through Weighted Model Counting. In Proceedings of the Sixteenth International Conference on Automated Planning and Scheduling (ICAPS 2006) (pp. 243-252). Menlo Park, USA: AAAI.

Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-22CE-0
We present a new algorithm for probabilistic planning with no observability. Our algorithm, called Probabilistic-FF, extends the heuristic forward-search machinery of Conformant-FF to problems with probabilistic uncertainty about both the initial state and action effects. Specifically, Probabilistic-FF combines Conformant-FF's techniques with a powerful machinery for weighted model counting in (weighted) CNFs, serving to elegantly define both the search space and the heuristic function. Our evaluation of Probabilistic-FF on several probabilistic domains shows an unprecedented, several orders of magnitude improvement over previous results in this area.