The Computational Complexity of Probabilistic Planning

We examine the computational complexity of testing and nding small plans in probabilistic planning domains with both at 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 de nitions of plan value. We show that problems of interest are complete for a variety of complexity classes: PL, P, NP, co-NP, PP, NP, co-NP, and PSPACE. In the process of proving that certain planning problems are complete for NP, we introduce a new basic NP-complete problem, E-Majsat, which generalizes the standard Boolean satis ability 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 e cient algorithms for a wide variety of problems.