Automated Hierarchical POMDP Construction through Data-mining Techniques

نویسندگان

  • G. Michael Youngblood
  • Edwin O. Heierman
  • Diane J. Cook
  • Lawrence B. Holder
چکیده

Markov models provide a useful representation of system behavioral actions and state observations, but they do not scale well. Utilizing a hierarchy and abstraction as in HHMMs improves scalability, but they are usually constructed manually using knowledge engineering techniques. In this paper, we introduce a new method of automatically constructing HHMMs and subsequent hierarchical POMDPs using the output of a sequential data-mining algorithm. We present the theory of this technique and frame it in a case study involving a learn-

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تاریخ انتشار 2005