Controlling the Hypothesis Space in Probabilistic Plan Recognition

Kabanza, Froduald; Filion, Julien; Benaskeur, Abder Rezak; Irandoust, Hengameh

Controlling the Hypothesis Space in Probabilistic Plan Recognition

Froduald Kabanza, Julien Filion, Abder Rezak Benaskeur, Hengameh Irandoust

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Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 2306-2311.

Abstract

The ability to understand the goals and plans of other agents is an important characteristic of intelligent behaviours in many contexts. One of the approaches used to endow agents with this capability is the weighted model counting approach. Given a plan library and a sequence of observations, this approach exhaustively enumerates plan execution models that are consistent with the observed behaviour. The probability that the agent might be pursuing a particular goal is then computed as a proportion of plan execution models satisfying the goal. The approach allows to recognize multiple interleaved plans, but suffers from a combinatorial explosion of plan execution models, which impedes its application to real-world domains. This paper presents a heuristic weighted model counting algorithm that limits the number of generated plan execution models in order to recognize goals quickly by computing their lower and upper bound likelihoods.

Froduald Kabanza, Julien Filion, Abder Rezak Benaskeur, and Hengameh Irandoust. 2013. Controlling the Hypothesis Space in Probabilistic Plan Recognition. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13. IJCAI, 2306–2311. https://ijcai.org/proceedings/2013/340

@inproceedings{ijcai2013p340,
  title     = {Controlling the Hypothesis Space in Probabilistic Plan Recognition},
  author    = {Kabanza, Froduald and Filion, Julien and Benaskeur, Abder Rezak and Irandoust, Hengameh},
  booktitle = {Proceedings of the Twenty-Third International Joint Conference on
               Artificial Intelligence, {IJCAI-13}},
  publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
  editor    = {Francesca Rossi},
  pages     = {2306--2311},
  year      = {2013},
  month     = {8},
  url       = {https://ijcai.org/proceedings/2013/340},
}

TY  - CPAPER
TI  - Controlling the Hypothesis Space in Probabilistic Plan Recognition
AU  - Kabanza, Froduald
AU  - Filion, Julien
AU  - Benaskeur, Abder Rezak
AU  - Irandoust, Hengameh
T2  - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13
PB  - AAAI Press / International Joint Conferences on Artificial Intelligence
PY  - 2013
SP  - 2306
EP  - 2311
UR  - https://ijcai.org/proceedings/2013/340
ER  -