Semantic analysis of rough logical formulas based on granular computing

Semantic Analysis of Logical formulas is to research into the meaning sets of logical formulas based on granular computing. In the paper, the meaning sets are axiomatized, and the reasoning systems are created to start with set axioms. The deductive reasoning in the set axiomatic systems is illustrated with real examples. The decomposing of granulation and the amalgamation of granules are also discussed. Index Terms-- Granular computing, Meaning sets of the logical formula, Meaning Set Axiomatization, Granular Deductive Reasoning.

[1]  Churn-Jung Liau Many-Valued Dynamic Logic for Qualitative Decision Theory , 1999, RSFDGrC.

[2]  Bo Zhang,et al.  The Quotient Space Theory of Problem Solving , 2003, Fundam. Informaticae.

[3]  Ewa Orlowska,et al.  Modal Logics in the Theory of Information Systems , 1984, Math. Log. Q..

[4]  Lotfi A. Zadeh,et al.  Fuzzy sets and information granularity , 1996 .

[5]  Tsau Young Lin,et al.  Data Mining: Granular Computing Approach , 1999, PAKDD.

[6]  Qing Liu,et al.  Design and Implement for Diagnosis Systems of Hemorheology on Blood Viscosity Syndrome Based on GrC , 2003, RSFDGrC.

[7]  Andrzej Skowron,et al.  Measures of Inclusion and Closeness of Information Granules: A Rough Set Approach , 2002, Rough Sets and Current Trends in Computing.

[8]  Qing Liu Granules and Reasoning Based on Granular Computing , 2003, IEA/AIE.

[9]  David Toman On completeness of multi-dimensional first-order temporal logics , 2003, 10th International Symposium on Temporal Representation and Reasoning, 2003 and Fourth International Conference on Temporal Logic. Proceedings..

[10]  Andrzej Skowron,et al.  Approximation of Information Granule Sets , 2000, Rough Sets and Current Trends in Computing.

[11]  Andrzej Skowron,et al.  Toward Intelligent Systems: Calculi of Information Granules , 2001, JSAI Workshops.

[12]  Ben J Hicks,et al.  SPIE - The International Society for Optical Engineering , 2001 .

[13]  Christian Freksa,et al.  Spatial and Temporal Structures in Cognitive Processes , 1997, Foundations of Computer Science: Potential - Theory - Cognition.

[14]  Tsau Young Lin,et al.  First-Order Rough Logic I: Approximate Reasoning via Rough Sets , 1996, Fundam. Informaticae.

[15]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[16]  Andrzej Skowron,et al.  Reasoning Based on Information Changes in Information Maps , 2003, RSFDGrC.

[17]  Lotfi A. Zadeh,et al.  Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems , 1998, Soft Comput..

[18]  Emmanuel Stefanakis,et al.  Spatio-Temporal Multicriteria Decision Making Under Uncertainty , 2001 .

[19]  Churn-Jung Liau Belief Reasoning, Revision and Fusion by Matrix Algebra , 2004, Rough Sets and Current Trends in Computing.

[20]  T. Y. Lin,et al.  Granular Computing on Binary Relations II Rough Set Representations and Belief Functions , 1998 .

[21]  Patrick Doherty,et al.  Information Granules for Intelligent Knowledge Structures , 2003, RSFDGrC.

[22]  Yiyu Yao,et al.  Induction of Classification Rules by Granular Computing , 2002, Rough Sets and Current Trends in Computing.

[23]  Hans Hermes,et al.  Introduction to mathematical logic , 1973, Universitext.

[24]  Ewa Orlowska,et al.  Logic of nondeterministic information , 1985, Stud Logica.

[25]  Qun Liu,et al.  Approximate reasoning based on granular computing in granular logic , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[26]  Liu Qing G-Logic and Its Resolution Reasoning , 2004 .