# Probabilistic State-Dependent Grammars for Plan Recognition

@inproceedings{Pynadath2000ProbabilisticSG, title={Probabilistic State-Dependent Grammars for Plan Recognition}, author={David V. Pynadath and Michael P. Wellman}, booktitle={UAI}, year={2000} }

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… Expand

#### 175 Citations

A probabilistic plan recognition algorithm based on plan tree grammars

- Mathematics, Computer Science
- Artif. Intell.
- 2009

It is shown that this clarifies several difficult issues in plan recognition including the execution of multiple interleaved root goals, partially ordered plans, and failing to observe actions. Expand

Controlling the Hypothesis Space in Probabilistic Plan Recognition

- Mathematics, Computer Science
- IJCAI
- 2013

A heuristic weighted model counting algorithm is presented that limits the number of generated plan execution models in order to recognize goals quickly by computing their lower and upper bound likelihoods. Expand

Abstract Hidden Markov Models for Online Probabilistic Plan Recognition

- Computer Science
- 2001

The structure of the stochastic model rep- resenting the execution of the general AMP is analyzed and an efficient hybrid Rao-Blackwellised sampling method for policy recognition that scales well with the number of levels in the plan hierarchy is provided. Expand

SLIM: Semi-Lazy Inference Mechanism for Plan Recognition

- Computer Science
- IJCAI
- 2016

A new and efficient algorithm for online plan recognition called SLIM (Semi-Lazy Inference Mechanism), which combines both a bottom-up and top-down parsing processes, which allow it to commit only to the minimum necessary actions in real-time, but still provide complete hypotheses post factum. Expand

Chapter 3 – Plan Recognition Using Statistical–Relational Models

- Computer Science
- 2014

This chapter introduces two new approaches to abductive plan recognition using Bayesian logic programs and Markov Logic Networks, and presents an extensive evaluation of these approaches on three benchmark datasets on plan recognition, comparing them with existing state-of-the-art methods. Expand

Extending Bayesian Logic Programs for Plan Recognition and Machine Reading

- Computer Science
- 2011

This work develops an approach to abductive plan recognition using BLPs and extends BLPs to use logical abduction to construct Bayesian networks and calls the resulting model Bayesian Abductive Logic Programs (BALPs), demonstrating that BALPs outperform the existing state-of-art methods like Markov Logic Networks (MLNs). Expand

Recognizing Plans with Loops Represented in a Lexicalized Grammar

- Computer Science
- AAAI
- 2011

It is shown how the loop-handling methods from formal grammars can be extended to the more general plan recognition problem and provide a method for encoding loops in an existing plan recognition system that scales linearly in the number of loop iterations. Expand

Plan Recognition in Continuous Domains

- Computer Science
- AAAI
- 2018

This work provides a formalization of recognition problems which admits continuous environments, as well as discrete domains, and shows that through mirroring— generalizing plan-recognition by planning—the authors can apply continuous-world motion planners in plan recognition. Expand

Considering State in Plan Recognition with Lexicalized Grammars

- Computer Science
- CogRob@AAAI
- 2012

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. Expand

Automatic Generation of Plan Libraries for Plan Recognition Performance Evaluation

- Computer Science
- 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)
- 2015

A mechanism to automatically generate arbitrarily complex plan libraries is developed, such plan library generation can be directed through a number of parameters to allow for systematic experimentation. Expand

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