# Probabilistic State-Dependent Grammars for Plan Recognition

@article{Pynadath2000ProbabilisticSG, title={Probabilistic State-Dependent Grammars for Plan Recognition}, author={David V. Pynadath and Michael P. Wellman}, journal={ArXiv}, year={2000}, volume={abs/1301.3888} }

Techniques for plan recognition under uncertainty require a stochastic model of the plangeneration process. We introduce probabilistic state-dependent grammars (PSDGs) to represent an agent's plan-generation process. The PSDG language model extends probabilistic contextfree 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…

## 181 Citations

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This paper documents extending the ELEXIR (Engine for LEXicalized Intent Recognition) system with a world model and allows a number of additions to the algorithm, most significantly conditioning probabilities for recognized plans on the state of the world during execution.

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