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Bidirectional LAO* Algorithm (A Faster Approach to Solve Goal-directed MDPs)

Uncertainty is a feature of many AI applications. While there are polynomial-time algorithms for planning in stochastic systems, planning is still slow, in part because most algorithms plan for all eventualities. Algorithms such as LAO* are able to find good or optimal policies more quickly when the starting state of the system is known.
In this thesis we present an extension to LAO*, called BLAO*. BLAO* is an extension of the LAO* algorithm to a bidirectional search. We show that BLAO* finds optimal or E-optimal solutions for goal-directed MDPs without necessarily evaluating the entire state space. BLAO* converges much faster than LAO* or RTDP on our benchmarks.

Identiferoai:union.ndltd.org:uky.edu/oai:uknowledge.uky.edu:gradschool_theses-1227
Date01 January 2004
CreatorsBhuma, Venkata Deepti Kiran
PublisherUKnowledge
Source SetsUniversity of Kentucky
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceUniversity of Kentucky Master's Theses

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