Refinement of history-based policies

  • Authors:
  • Jorge Lobo;Jiefei Ma;Alessandra Russo;Emil Lupu;Seraphin Calo;Morris Sloman

  • Affiliations:
  • IBM T.J. Watson Research Center, New York;Department of Computing, Imperial College London, United Kingdom;Department of Computing, Imperial College London, United Kingdom;Department of Computing, Imperial College London, United Kingdom;IBM T.J. Watson Research Center, New York;Department of Computing, Imperial College London, United Kingdom

  • Venue:
  • Logic programming, knowledge representation, and nonmonotonic reasoning
  • Year:
  • 2011

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Abstract

We propose an efficient method to evaluate a large class of history-based policies written as logic programs. To achieve this, we dynamically compute, from a given policy set, a finite subset of the history required and sufficient to evaluate the policies. We maintain this history by monitoring rules and transform the policies into a non history-based form. We further formally prove that evaluating history-based policies can be reduced to an equivalent, but more efficient, evaluation of the non history-based policies together with the monitoring rules.