The complexity of plan existence and evaluation in robabilistic domains

Authors:
Judy Goldsmith;Michael L. Littman;Martin Mundhenk
Affiliations:
Dept. of Computer Science, University of Kentucky, Lexington, KY;Dept. of Computer Science, Duke University, Durham, NC;FB4 - Theoretische Informatik, Universität Trier, Germany
Venue:
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Year:
1997

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Abstract

We examine the computational complexity of testing and finding small plans in probabilistic planning domains with succinct representations. We find that many problems of interest are complete for a variety of complexity classes: NP, co-NP, PP, NPPP, co-NP PP, and PSPACE. Of these, the probabilistic classes PP and NPPP are likely to be of special interest in the field of uncertainty in artificial intelligence and are deserving of additional study. These results suggest a fruitful direction of future algorithmic development.