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Hybrid Symbolic-Probabilistic Plan Recognizer: Initial steps

Dorit Avrahami-Zilberbrand and Gal A. Kaminka. Hybrid Symbolic-Probabilistic Plan Recognizer: Initial steps. In Proceedings of the AAAI Workshop on Modeling Others from Observations (MOO-06), 2006.

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Abstract

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.

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BibTeX

@InProceedings{moo06dorit, 
  author = 	 {Dorit Avrahami-Zilberbrand and Gal A. Kaminka}, 
  title = 	 {Hybrid Symbolic-Probabilistic Plan Recognizer: Initial steps}, 
  OPTcrossref =  {}, 
  OPTkey = 	 {}, 
  booktitle = MOO-06, 
  OPTpages = 	 {}, 
  year = 	 {2006}, 
  abstract = { 
     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. 
 }, 
  wwwnote = {}, 
  OPTeditor = 	 {}, 
  OPTvolume = 	 {}, 
  OPTnumber = 	 {}, 
  OPTseries = 	 {}, 
  OPTaddress = 	 {}, 
  OPTmonth = 	 {}, 
  OPTorganization = {}, 
  OPTpublisher = {}, 
  OPTnote = 	 {}, 
  OPTannote = 	 {} 
} 

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