The computational complexity of probabilistic planning

Authors:
Michael L. Littman;Judy Goldsmith;Martin Mundhenk
Affiliations:
Department of Computer Science, Duke University, Durham, NC;Department of Computer Science, University of Kentucky, Lexington, KY;FB4 - Theoretische Informatik, Universität Trier, Trier, Germany
Venue:
Journal of Artificial Intelligence Research
Year:
1998

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Abstract

We examine the computational complexity of testing and finding small plans in probabilistic planning domains with both flat and propositional representations. The complexity of plan evaluation and existence varies with the plan type sought; we examine totally ordered plans, acyclic plans, and looping plans, and partially ordered plans under three natural definitions of plan value. We show that problems of interest are complete for a variety of complexity classes: PL, P, NP, co-NP, PP, NPPP, co-NPPP, and PSPACE. In the process of proving that certain planning problems are complete for NPPP, we introduce a new basic NPPP -complete problem, E-MAJSAT, which generalizes the standard Boolean satisfiability problem to computations involving probabilistic quantities; our results suggest that the development of good heuristics for E-MAJSAT could be important for the creation of efficient algorithms for a wide variety of problems.