Hybrid symbolic-probabilistic plan recognizer: Initial steps

Dorit Avrahami-Zilberbrand, Gal A. Kaminka

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations


It is important for agents to model other agents' unobserved plans and goals, based on their observable actions, a process known as plan recognition. Plan recognition often takes the form of matching observations of an agent's actions to a plan-library, a model of possible plans selected by the agent. In this paper, we present efficient algorithms that handle a number of key capabilities implied by plan recognition applications, in the context of hybrid symbolic-probabilistic recognizer. The central idea behind the hybrid approach is to combine the symbolic approach with probabilistic inference: the symbolic recognizer efficiently filters inconsistent hypotheses, passing only the consistent hypotheses to a probabilistic inference engine. There are few investigations that utilize an hybrid symbolic-probabilistic approach. The advantage of this kind of inference is potentially enormous. First, it can be highly efficient. Second, it can efficiently deal with richer class of plan recognition challenges, such as recognition based on duration of behaviors, recognition despite intermittently lost observations, and recognition of interleaved plans.

Original languageEnglish
Title of host publicationModeling Others from Observation - Papers from the AAAI Workshop, Technical Report
Number of pages7
StatePublished - 2006
Event2006 AAAI Workshop - Boston, MA, United States
Duration: 16 Jul 200617 Jul 2006

Publication series

NameAAAI Workshop - Technical Report


Conference2006 AAAI Workshop
Country/TerritoryUnited States
CityBoston, MA


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