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arXiv · 1111.0044

Probabilistic Planning via Heuristic Forward Search and Weighted Model Counting

C. Domshlak·J. Hoffmann
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Abstract

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-FFs 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 shows its fine scalability in a range of probabilistic domains, constituting a several orders of magnitude improvement over previous results in this area. We use a problematic case to point out the main open issue to be addressed by further research.

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C. Domshlak, J. Hoffmann. 2011-10-31. Probabilistic Planning via Heuristic Forward Search and Weighted Model Counting. https://doi.org/10.1613/jair.2289

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