Using plan recognition in human-computer collaboration
UM '99 Proceedings of the seventh international conference on User modeling
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Languages and Machines: An Introduction to the Theory of Computer Science (3rd Edition)
Languages and Machines: An Introduction to the Theory of Computer Science (3rd Edition)
A probabilistic plan recognition algorithm based on plan tree grammars
Artificial Intelligence
Identifying terrorist activity with AI plan recognition technology
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
Hypothesis pruning and ranking for large plan recognition problems
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
Recognizing plan/goal abandonment
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Fast and complete symbolic plan recognition
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Deduction as parsing: tractable classification in the KL-ONE framework
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Probabilistic state-dependent grammars for plan recognition
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
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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.