Contingent Planning Under Uncertainty via Stochastic Satis ability

We describe two new probabilistic planning techniques|c-maxplan and zander|that generate contingent plans in probabilistic propositional domains. Both operate by transforming the planning problem into a stochastic satis ability problem and solving that problem instead. c-maxplan encodes the problem as an E-Majsat instance, while zander encodes the problem as an S-Sat instance. Although S-Sat problems are in a higher complexity class than E-Majsat problems, the problem encodings produced by zander are substantially more compact and appear to be easier to solve than the corresponding E-Majsat encodings. Preliminary results for zander indicate that it is competitive with existing planners on a variety of problems.