The Complexity of Plan Existence and Evaluation in Probabilistic Domains
Artificial Intelligence
2013-02-08 v1
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
Cite
@article{arxiv.1302.1540,
title = {The Complexity of Plan Existence and Evaluation in Probabilistic Domains},
author = {Judy Goldsmith and Michael L. Littman and Martin Mundhenk},
journal= {arXiv preprint arXiv:1302.1540},
year = {2013}
}
Comments
Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997)