Acting Optimally in Partially Observable Stochastic Domains

  title={Acting Optimally in Partially Observable Stochastic Domains},
  author={Anthony R. Cassandra and Leslie Pack Kaelbling and Michael L. Littman},
In this paper, we describe the partially observable Markov decision process (pomdp) approach to nding optimal or near-optimal control strategies for partially observable stochastic environments, given a complete model of the environment. The pomdp approach was originally developed in the operations research community and provides a formal basis for planning problems that have been of interest to the AI community. We found the existing algorithms for computing optimal control strategies to be… CONTINUE READING
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