Uncertainty in Artificial Intelligence
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The Automated Mapping of Plans for Plan Recognition
Marcus Huber, Edmund Durfee, Michael Wellman
Abstract:
To coordinate with other agents in its environment, an agent needs models of what the other agents are trying to do. When communication is impossible or expensive, this information must be acquired indirectly via plan recognition. Typical approaches to plan recognition start with a specification of the possible plans the other agents may be following, and develop special techniques for discriminating among the possibilities. Perhaps more desirable would be a uniform procedure for mapping plans to general structures supporting inference based on uncertain and incomplete observations. In this paper, we describe a set of methods for converting plans represented in a flexible procedural language to observation models represented as probabilistic belief networks.
Keywords: Probabilistic networks, plan recognition, distributed AI.
Pages: 344-351
PS Link: ftp://ftp.eecs.umich.edu/people/wellman/uai94huber.ps.Z
PDF Link: /papers/94/p344-huber.pdf
BibTex:
@INPROCEEDINGS{Huber94,
AUTHOR = "Marcus Huber and Edmund Durfee and Michael Wellman",
TITLE = "The Automated Mapping of Plans for Plan Recognition",
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 = "344--351"
}


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