First-order rough logic I: approximate reasoning via rough sets
Fundamenta Informaticae - Special issue: rough sets
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
Data Mining and Machine Oriented Modeling: A Granular Computing Approach
Applied Intelligence
Extracting Patterns Using Information Granules: A Brief Introduction
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
The OI-Resolution of Operator Rough Logic
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Induction of Classification Rules by Granular Computing
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Data Mining: Granular Computing Approach
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
The Resolution for Ruogh Propositional Logic with Lower (L) and Upper (h) Approximate Operators
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
A Generalized Decision Logic in Interval-Set-Valued Information Tables
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Constructing rough mereological granules of classifying rules and classifying algorithms
Technologies for constructing intelligent systems
Granules and reasoning based on granular computing
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
On generalizing rough set theory
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
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
A Study in Granular Computing: On Classifiers Induced from Granular Reflections of Data
Transactions on Rough Sets IX
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
The impact of rough set research in China: in commemoration of professor Zdzisław Pawlak
Transactions on rough sets VI
Transactions on computational science II
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We propose a higher order logic called as the granular logic. This logic is introduced as a tool for investigating properties of granular computing. In particular, constants of this logic are of the form m(F), where F is a formula (e.g., Boolean combination of descriptors) in a given information system. Truth values of the granular formula are discussed. The truth value of a given formula in a given model is defined by a degree to which the meaning of this formula in the given model is close to the universe of objects. Our approach generalizes the rough truth concept introduced by Zdzisław Pawlak in 1987. We present an axiomatization of granular logic. The resolution reasoning in the axiomatic systems is illustrated by examples, and the resolution soundness is also proved