The computational complexity of probabilistic planning

Michael L. Littman, Judy Goldsmith, Martin Mundhenk

Research output: Contribution to journalArticlepeer-review

146 Scopus citations


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.

Original languageEnglish
Pages (from-to)1-36
Number of pages36
JournalJournal of Artificial Intelligence Research
StatePublished - 1998

ASJC Scopus subject areas

  • Artificial Intelligence


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