The Computational Complexity of Probabilistic Planning

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dc.creatorLittman, M. L.
dc.creatorGoldsmith, J.
dc.creatorMundhenk, M.
dc.date1998-08-01
dc.date.accessioned2026-07-07T03:23:28Z
dc.date.available2026-07-07T03:23:28Z
dc.descriptionWe 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, NP^PP, co-NP^PP, and PSPACE. In the process of proving that certain planning problems are complete for NP^PP, we introduce a new basic NP^PP-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.
dc.descriptionSee http://www.jair.org/ for any accompanying files
dc.identifierhttps://arxiv.org/abs/cs/9808101
dc.identifierhttp://arxiv.org/abs/cs/9808101
dc.identifierJournal of Artificial Intelligence Research, Vol 9, (1998), 1-36
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32955
dc.subjectArtificial Intelligence
dc.titleThe Computational Complexity of Probabilistic Planning
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