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.
| Identifer | oai:union.ndltd.org:uky.edu/oai:uknowledge.uky.edu:gradschool_theses-1227 |
| Date | 01 January 2004 |
| Creators | Bhuma, Venkata Deepti Kiran |
| Publisher | UKnowledge |
| Source Sets | University of Kentucky |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | University of Kentucky Master's Theses |
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