Using approximate reasoning to represent default knowledge
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Knowledge and common knowledge in a distributed environment
Journal of the ACM (JACM)
From rough set theory to evidence theory
Advances in the Dempster-Shafer theory of evidence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Principles of Database Systems
Principles of Database Systems
Boolean Reasoning for Decision Rules Generation
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
Approximation Methods for Knowledge Representation Systems
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
Modal Logics for Knowledge Representation Systems
Proceedings of the Symposium on Logical Foundations of Computer Science: Logic at Botik '89
Rough Logic for Multi-Agent Systems
International Conference Logic at Work on Knowledge Representation and Reasoning Under Uncertainty, Logic at Work
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In this paper, we would like to investigate the relationship between evidential structures (ES)—the basic qualitative structures of Dempster-Shafer theory, and the data table based knowledge representation systems(KRS) subject to rough set analysis. It is shown that an ES has a natural representation as a data table and from a given data table and two of its attributes, an ES can be extracted. We also show that some important operations on ES's can be realized in relational algebra. The results are then generalized to the fuzzy case. Consequently, we further clarify the connection between evidence theory and rough set theory.