Sequential plan recognition

Reuth Mirsky, Roni Stern, Ya'Akov Kobi Gal, Meir Kalech

Research output: Contribution to journalConference articlepeer-review

11 Scopus citations


Plan recognition algorithms infer agents' plans from their observed actions. Due to imperfect knowledge about the agent's behavior and the environment, it is often the case that there are multiple hypotheses about an agent's plans that are consistent with the observations, though only one of these hypotheses is correct. This paper addresses the problem of how to disambiguate between hypotheses, by querying the acting agent about whether a candidate plan in one of the hypotheses matches its intentions. This process is performed sequentially and used to update the set of possible hypotheses during the recognition process. The paper defines the sequential plan recognition process (SPRP), which seeks to reduce the number of hypotheses using a minimal number of queries. We propose a number of policies for the SPRP which use maximum likelihood and information gain to choose which plan to query. We show this approach works well in practice on two domains from the literature, significantly reducing the number of hypotheses using fewer queries than a baseline approach. Our results can inform the design of future plan recognition systems that interleave the recognition process with intelligent interventions of their users.

Original languageEnglish
Pages (from-to)401-407
Number of pages7
JournalIJCAI International Joint Conference on Artificial Intelligence
StatePublished - 2016
Externally publishedYes
Event25th International Joint Conference on Artificial Intelligence, IJCAI 2016 - New York, United States
Duration: 9 Jul 201615 Jul 2016

Bibliographical note

Funding Information:
This research was funded in part by ISF grant numbers 363/12 and 1276/12, and by EU FP7 FET project no. 60085. R.M. is a recipient of the Pratt fellowship at the Ben-Gurion University of the Negev


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