State-Based Regression with Sensing and Knowledge
Received:March 22, 2009  Revised:July 16, 2010  Download PDF
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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Fund:This effort is sponsored by the DTO’s AQUAINT program under award number N61339-06-C-0143; the Knowledge Fusion Center of the Army Research Laboratory under contract number DAAD-03- 2-0034; NSF under grant number 0412000 and from ONR-MURI number N00014-07-1-1049; NSF grants IIS-0812267, EIA-0220590, and CNS-0454066.
Abstract: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.
keywords:regression  plans  knowledge  sensing
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