Fast and complete symbolic plan recognition

Dorit Avrahami-Zilberbrand, Gal A. Kaminka

Research output: Contribution to journalConference articlepeer-review

95 Scopus citations


Recent applications of plan recognition face several open challenges: (i) matching observations to the plan library is costly, especially with complex multi-featured observations; (ii) computing recognition hypotheses is expensive. We present techniques for addressing these challenges. First, we show a novel application of machine-learning decision-tree to efficiently map multi-featured observations to matching plan steps. Second, we provide efficient lazy-commitment recognition algorithms that avoid enumerating hypotheses with every observation, instead only carrying out bookkeeping incrementally. The algorithms answer queries as to the current state of the agent, as well as its history of selected states. We provide empirical results demonstrating their efficiency and capabilities.

Original languageEnglish
Pages (from-to)653-658
Number of pages6
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - 2005
Event19th International Joint Conference on Artificial Intelligence, IJCAI 2005 - Edinburgh, United Kingdom
Duration: 30 Jul 20055 Aug 2005


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