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. |
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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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