Gal A. Kaminka's Publications

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Dynamics Based Control with an Application to Area-Sweeping Problems

Zinovi Rabinovich, Jeffrey S. Rosenschein, and Gal A. Kaminka. Dynamics Based Control with an Application to Area-Sweeping Problems. In Proceedings of the Sixth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-07), 2007.

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Abstract

In this paper we introduce Dynamics Based Control (DBC), an approach to planning and control of an agent in stochastic environments. Unlike existing approaches, which seek to optimize expected rewards (e.g., in Partially Observable Markov Decision Problems (POMDPs)), DBC optimizes system behavior towards specified system dynamics. We show that a recently developed planning and control approach, Extended Markov Tracking (EMT) is an instantiation of DBC. EMT employs greedy action selection to provide an efficient control algorithm in Markovian environments. We exploit this efficiency in a set of experiments that applied multi-target EMT to a class of area-sweeping problems (searching for moving targets). We show that such problems can be naturally defined and efficiently solved using the DBC framework, and its EMT instantiation.

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BibTeX

@InProceedings{aamas07zinovi, 
  author = 	 {Zinovi Rabinovich and Jeffrey S. Rosenschein and Gal A. Kaminka}, 
  title = 	 {Dynamics Based Control with an Application to Area-Sweeping Problems}, 
  OPTcrossref =  {}, 
  OPTkey = 	 {}, 
  booktitle = AAMAS-07, 
  OPTpages = 	 {In press}, 
  year = 	 {2007}, 
  abstract = { 
In this paper we introduce Dynamics Based Control (DBC), an approach to planning and 
    control of an agent in stochastic environments. Unlike existing approaches, which 
    seek to optimize expected rewards (e.g., in Partially Observable Markov Decision 
    Problems (POMDPs)), DBC optimizes system behavior towards specified system dynamics.  
    We show that a recently developed planning and control approach, Extended 
    Markov Tracking (EMT)  is an instantiation of DBC.  EMT employs greedy action 
    selection to provide an efficient control algorithm in Markovian environments.  We 
    exploit this efficiency in a set of experiments that applied multi-target EMT to a 
    class of area-sweeping problems (searching for moving targets). We show that such 
    problems can be naturally defined and efficiently solved using the DBC framework, and 
    its EMT instantiation. 
  }, 
  wwwnote = {}, 
  OPTeditor = 	 {}, 
  OPTvolume = 	 {}, 
  OPTnumber = 	 {}, 
  OPTseries = 	 {}, 
  OPTaddress = 	 {}, 
  OPTmonth = 	 {}, 
  OPTorganization = {}, 
  OPTpublisher = {}, 
  OPTannote = 	 {} 
} 

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