Uncertainty in Artificial Intelligence
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Integrating Planning and Execution in Stochastic Domains
Richard Dearden, Craig Boutilier
Abstract:
We investigate planning in time-critical domains represented as Markov Decision Processes, showing that search based techniques can be a very powerful method for finding close to optimal plans. To reduce the computational cost of planning in these domains, we execute actions as we construct the plan, and sacrifice optimality by searching to a fixed depth and using a heuristic function to estimate the value of states. Although this paper concentrates on the search algorithm, we also discuss ways of constructing heuristic functions suitable for this approach. Our results show that by interleaving search and execution, close to optimal policies can be found without the computational requirements of other approaches.
Keywords:
Pages: 162-169
PS Link:
PDF Link: /papers/94/p162-dearden.pdf
BibTex:
@INPROCEEDINGS{Dearden94,
AUTHOR = "Richard Dearden and Craig Boutilier",
TITLE = "Integrating Planning and Execution in Stochastic Domains",
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 = "162--169"
}


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