State-Based Regression with Sensing and Knowledge

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    This paper develops a state-based regression method for planning domains with sensing operators and a representation of the knowledge of the planning agent. The language includes primitive actions, sensing actions, and conditional plans. The regression operator is direct in that it does not depend on a progression operator for its formulation. We prove the soundness and completeness of the regression formulation with respect to the definition of progression and the semantics of a propositional modal logic of knowledge. The approach is illustrated with a running example that can not be handled by related methods that utilize an approximation of knowledge instead of the full semantics of knowledge as is used here. It is our expectation that this work will serve as the foundation for the extension of work on state-based regression planning to include sensing and knowledge as well.

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Richard Scherl, Tran Cao Son, Chitta Baral. State-Based Regression with Sensing and Knowledge. International Journal of Software and Informatics, 2009,3(1):3~30

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  • Received:March 22,2009
  • Revised:July 16,2010
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