Probabilistic Planning via Heuristic Forward Search and Weighted Model Counting

Details Probabilistic Planning via Heuristic Forward Search and Weighted Model Counting Probabilistic Planning via Heuristic Forward Search and Weighted Model Counting

2011-10-31

arxiv.org/abs/1111.0044v1

Journal Of Artificial Intelligence Research, Volume 30, pages 565-620, 2007

Computer Science

Artificial Intelligence

Scientific paper

10.1613/jair.2289

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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