Siddharth Srivastava and Xiang Cheng and Stuart J. Russell and Avi Pfeffer

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2013-243

December 25, 2013

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-243.pdf

Interest in relational and first-order languages for probability models has grown rapidly in recent years, and with it the possibility of extending such languages to handle decision processes---both fully and partially observable. We examine the problem of extending a first-order, open-universe language to describe POMDPs and identify non-trivial representational issues in describing an agent's capability for observation and action---issues that were avoided in previous work only by making strong and restrictive assumptions. We present a method for representing actions and observations that respects formal specifications of the sensors and actuators available to an agent, and show how to handle cases---such as seeing an object and picking it up---that could not previously be represented. Finally, we argue that in many cases open-universe POMDPs require belief-state policies rather than automata policies. We present an algorithm and experimental results for evaluating such policies for open-unverse POMDPs.

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BibTeX citation:

@techreport{Srivastava:EECS-2013-243,
    Author= {Srivastava, Siddharth and Cheng, Xiang and Russell, Stuart J. and Pfeffer, Avi},
    Title= {First-Order Open-Universe POMDPs: Formulation and Algorithms},
    Year= {2013},
    Month= {Dec},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-243.html},
    Number= {UCB/EECS-2013-243},
    Abstract= { Interest in relational and first-order languages for probability
  models has grown rapidly in recent years, and with it the
  possibility of extending such languages to handle decision
  processes---both fully and partially observable.  We examine the
  problem of extending a first-order, open-universe language to
  describe POMDPs and identify non-trivial representational issues in
  describing an agent's capability for observation and action---issues
  that were avoided in previous work only by making strong and
  restrictive assumptions. We present a method for representing
  actions and observations that respects formal specifications of the
  sensors and actuators available to an agent, and show how to handle
  cases---such as seeing an object and picking it up---that could not
  previously be represented. Finally, we argue that in many cases
  open-universe POMDPs require belief-state policies rather than
  automata policies. We present an algorithm and experimental results
  for evaluating such policies for open-unverse POMDPs.},
}

EndNote citation:

%0 Report
%A Srivastava, Siddharth 
%A Cheng, Xiang 
%A Russell, Stuart J. 
%A Pfeffer, Avi 
%T First-Order Open-Universe POMDPs: Formulation and Algorithms
%I EECS Department, University of California, Berkeley
%D 2013
%8 December 25
%@ UCB/EECS-2013-243
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2013/EECS-2013-243.html
%F Srivastava:EECS-2013-243