Abstract

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 satisfiability 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. Introduction When planning under uncertainty, any information about the state of the world is precious. A contingent plan is one that can make action choices contingent on such information. In this paper, we present an implemented framework for contingent pl...

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