Uncertainty in Artificial Intelligence
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Path Planning under Time-Dependent Uncertainty
Michael Wellman, Matthew Ford, Kenneth Larson
Abstract:
Standard algorithms for finding the shortest path in a graph require that the cost of a path be additive in edge costs, and typically assume that costs are deterministic. We consider the problem of uncertain edge costs, with potential probabilistic dependencies among the costs. Although these dependencies violate the standard dynamic-programming decomposition, we identify a weaker stochastic consistency condition that justifies a generalized dynamic-programming approach based on stochastic dominance. We present a revised path-planning algorithm and prove that it produces optimal paths under time-dependent uncertain costs. We test the algorithm by applying it to a model of stochastic bus networks, and present empirical performance results comparing it to some alternatives. Finally, we consider extensions of these concepts to a more general class of problems of heuristic search under uncertainty.
Keywords: Search, path planning, stochastic dominance.
Pages: 532-539
PS Link: ftp://ftp.eecs.umich.edu/people/wellman/uai95path.ps.Z
PDF Link: /papers/95/p532-wellman.pdf
BibTex:
@INPROCEEDINGS{Wellman95,
AUTHOR = "Michael Wellman and Matthew Ford and Kenneth Larson",
TITLE = "Path Planning under Time-Dependent Uncertainty",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "532--539"
}


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