Gal A. Kaminka: Publications

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Fast and complete plan recognition: Allowing for duration, interleaved execution, and lossy observations

Dorit Avrahami-Zilberbrand, Gal A. Kaminka, and Hila Zarosim. Fast and complete plan recognition: Allowing for duration, interleaved execution, and lossy observations. In Proceedings of the IJCAI Workshop on Modeling Others from Observations (MOO-05), 2005.

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Abstract

It is important for agents to model other agents’ unobserved plans and goals, based on their observable actions. This process of modeling others based on observations is known as plan-recognition. Plan recognition has been studied for many years. It 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. However, there are several open key challenges in modern plan recognition: (i) handling lossy observations (where an observation or a component of an observation is intermittently lost); (ii) dealing with plan execution duration constraints; and (iii) interleaved plans (where an agent interrupts a plan for another, only to return to the first later). In this paper, we present efficient algorithms that address these challenges, in the context of symbolic plan recognition. The algorithms allow (i) efficient matching of (possibly lossy) observations to a plan library; (ii) efficient computation of all recognition hypotheses consistent with the observations, subject to interleaving and duration constraints.

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BibTeX

@InProceedings{moo05dorit, 
  author = 	 {Dorit Avrahami-Zilberbrand and Gal A. Kaminka and Hila Zarosim}, 
  title = 	 {Fast and complete plan recognition: Allowing for duration, interleaved execution, and lossy observations}, 
  OPTcrossref =  {}, 
  OPTkey = 	 {}, 
  booktitle = MOO-05, 
  OPTpages = 	 {}, 
  year = 	 {2005}, 
  abstract = { 
   It is important for agents to model other agents’ unobserved plans 
   and goals, based on their observable actions. This process of 
   modeling others based on observations is known as 
   plan-recognition. Plan recognition has been studied for many 
   years. It 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. However, there are several open key 
   challenges in modern plan recognition: (i) handling lossy 
   observations (where an observation or a component of an observation 
   is intermittently lost); (ii) dealing with plan execution 
   duration constraints; and (iii) interleaved plans (where an agent 
   interrupts a plan for another, only to return to the first 
   later). In this paper, we present efficient algorithms that address 
   these challenges, in the context of symbolic plan recognition. The 
   algorithms allow (i) efficient matching of (possibly lossy) 
   observations to a plan library; (ii) efficient computation of all 
   recognition hypotheses consistent with the observations, subject 
   to interleaving and duration constraints. 
  }, 
  wwwnote = {}, 
  OPTeditor = 	 {}, 
  OPTvolume = 	 {}, 
  OPTnumber = 	 {}, 
  OPTseries = 	 {}, 
  OPTaddress = 	 {}, 
  OPTmonth = 	 {}, 
  OPTorganization = {}, 
  OPTpublisher = {}, 
  OPTnote = 	 {}, 
  OPTannote = 	 {} 
} 

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