Corpus ID: 14203192

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
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175 Citations
Highly Influential Citations
8
Background Citations
74
Methods Citations
41
Results Citations
2

175 Citations

Citation Type

Citation Type

A probabilistic plan recognition algorithm based on plan tree grammars
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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
  • C. Geib
  • Computer Science
  • CogRob@AAAI
  • 2012
TLDR
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
TLDR
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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References

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New inference algorithms that generate a Bayesian network representation of the PCFG distribution and specialized algorithms that exploit the particular independence properties of the PSDG language to maintain a more efficient summary of evidence in the form of a belief state are presented. Expand
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