Uncertainty in Artificial Intelligence
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Probabilistic State-Dependent Grammars for Plan Recognition
David Pynadath, Michael Wellman
Abstract:
Techniques for plan recognition under uncertainty require a stochastic model of the plan-generation process. We introduce Probabilistic State-Dependent Grammars (PSDGs) to represent an agent's plan-generation process. The PSDG language model extends probabilistic context-free grammars (PCFGs) by allowing production probabilities to depend on an explicit model of the planning agent's internal and external state. Given a PSDG description of the plan-generation process, we can then use inference algorithms that exploit the particular independence properties of the PSDG language to efficiently answer plan-recognition queries. The combination of the PSDG language model and inference algorithms extends the range of plan-recognition domains for which practical probabilistic inference is possible, as illustrated by applications in traffic monitoring and air combat.
Keywords: plan recognition, probabilistic grammars
Pages: 507-514
PS Link: http://www.isi.edu/~pynadath/Research/uai00.ps.gz
PDF Link: /papers/00/p507-pynadath.pdf
BibTex:
@INPROCEEDINGS{Pynadath00,
AUTHOR = "David Pynadath and Michael Wellman",
TITLE = "Probabilistic State-Dependent Grammars for Plan Recognition",
BOOKTITLE = "Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2000",
PAGES = "507--514"
}


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