Contingent planning under uncertainty via stochastic satisfiability

Authors:
Stephen M. Majercik;Michael L. Littman
Affiliations:
Bowdoin College, Brunswick, ME;Rutgers University, Piscataway, NJ
Venue:
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Year:
2003

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Abstract

We describe a new planning technique that efficiently solves probabilistic propositional contingent planning problems by converting them into instances of stochastic satisfiability (SSAT) and solving these problems instead. We make fundamental contributions in two areas: the solution of SSAT problems and the solution of stochastic planning problems. This is the first work extending the planning-as-satisfiability paradigm to stochastic domains. Our planner, ZANDER, can solve arbitrary, goal-oriented, finite-horizon partially observable Markov decision processes (POMDPS). An empirical study comparing ZANDER to seven other leading planners shows that its performance is competitive on a range of problems.