Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Symbolic Logic and Mechanical Theorem Proving
Symbolic Logic and Mechanical Theorem Proving
lambda-Level Rough Equality Relation and the Inference of Rough Paramodulation
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Reasoning about Information Granules Based on Rough Logic
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Granules and reasoning based on granular computing
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
Granulations Based on Semantics of Rough Logical Formulas and Its Reasoning
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Theoretical study of granular computing
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Granular logic with closeness relation "∼λ" and its reasoning
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
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Based on rough set theory, this paper establishes operator space [ξ*, ξ*]. It is also a subset on truth value interval [0,1]. The operators is put in the front of the formulas to produce the manyvalued logic called operator rough logic(ORL). It defines OI-valid and OI-inconsistent, OI-resolution of the logic, where OI is an abbreviation of Operator Interval. And it also proves the soundness theorem of the logic resolution.