Probabilistic State-Dependent Grammars for Plan Recognition

David V. Pynadath; Michael P. Wellman

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arXiv · 1301.3888

Probabilistic State-Dependent Grammars for Plan Recognition

David V. Pynadath·Michael P. Wellman

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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.

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David V. Pynadath, Michael P. Wellman. 2013-01-16. Probabilistic State-Dependent Grammars for Plan Recognition. https://arxiv.org/abs/1301.3888

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