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We present a new algorithm for probabilistic planning with no observability.Our algorithm, called Probabilistic-FF, extends the heuristic forward-searchmachinery of Conformant-FF to problems with probabilistic uncertainty aboutboth the initial state and action effects. Specifically, Probabilistic-FFcombines Conformant-FFs techniques with a powerful machinery for weighted modelcounting in (weighted) CNFs, serving to elegantly define both the search spaceand the heuristic function. Our evaluation of Probabilistic-FF shows its finescalability in a range of probabilistic domains, constituting a several ordersof magnitude improvement over previous results in this area. We use aproblematic case to point out the main open issue to be addressed by furtherresearch.

Probabilistic Planning via Heuristic Forward Search and Weighted Model Counting