Uncertainty in Artificial Intelligence
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Cost Sensitive Reachability Heuristics for Handling State Uncertainty
Daniel Bryce, Subbarao Kambhampati
Abstract:
While POMDPs provide a general platform for non-deterministic conditional planning under a variety of quality metrics they have limited scalability. On the other hand, non-deterministic conditional planners scale very well, but many lack the ability to optimize plan quality metrics. We present a novel generalization of planning graph based heuristics that helps conditional planners both scale and generate high quality plans when using actions with nonuniform costs. We make empirical comparisons with two state of the art planners to show the benefit of our techniques.
Keywords:
Pages: 60-68
PS Link:
PDF Link: /papers/05/p60-bryce.pdf
BibTex:
@INPROCEEDINGS{Bryce05,
AUTHOR = "Daniel Bryce and Subbarao Kambhampati",
TITLE = "Cost Sensitive Reachability Heuristics for Handling State Uncertainty",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "60--68"
}


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