Uncertainty in Artificial Intelligence
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The Complexity of Plan Existence and Evaluation in Probabilistic Domains
Judy Goldsmith, Michael Littman, Martin Mundhenk
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, NP^PP, co-NP^PP, and PSPACE. Of these, the probabilistic classes PP and NP^PP 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.
Keywords: Probabilistic planning, complexity, MDPs.
Pages: 182-189
PS Link: http://www.cs.duke.edu/~mlittman/papers/uai97-nppp.ps
PDF Link: /papers/97/p182-goldsmith.pdf
BibTex:
@INPROCEEDINGS{Goldsmith97,
AUTHOR = "Judy Goldsmith and Michael Littman and Martin Mundhenk",
TITLE = "The Complexity of Plan Existence and Evaluation in Probabilistic Domains",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "182--189"
}


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