Uncertainty in Artificial Intelligence
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Epsilon-Safe Planning
Robert Goldman, Mark Boddy
Abstract:
We introduce an approach to high-level conditional planning we call epsilon-safe planning. This probabilistic approach commits us to planning to meet some specified goal with a probability of success of at least 1-epsilon for some user-supplied epsilon. We describe several algorithms for epsilon-safe planning based on conditional planners. The two conditional planners we discuss are Peot and Smith's nonlinear conditional planner, CNLP, and our own linear conditional planner, PLINTH. We present a straightforward extension to conditional planners for which computing the necessary probabilities is simple, employing a commonly-made but perhaps overly-strong independence assumption. We also discuss a second approach to epsilon-safe planning which relaxes this independence assumption, involving the incremental construction of a probability dependence model in conjunction with the construction of the plan graph.
Keywords: Planning, decision-theoretic planning, linear planning, nonlinear planning, probabili
Pages: 253-261
PS Link: http://www.geocities.com/rpgoldman/papers/epsilon-planning.ps
PDF Link: /papers/94/p253-goldman.pdf
BibTex:
@INPROCEEDINGS{Goldman94,
AUTHOR = "Robert Goldman and Mark Boddy",
TITLE = "Epsilon-Safe Planning",
BOOKTITLE = "Proceedings of the Tenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-94)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1994",
PAGES = "253--261"
}


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