Compensatory Fuzzy Logic

In multivalued logics over [0,1] the conjunction is usually defined as a continuous, associative, and symmetric connective satisfying (1), and the disjunction is defined as an operator implied by De Morgan‟s Laws according to the definition of conjunction. In such circumstances, the conjunction satisfies the t-norm property, and the disjunction satisfies the t-conorm property.

Notice that properties (1) and (2) lead to the conclusion that the truth-value of the conjunction is equal or less than those of its components; and the truth value of the disjunction is equal or greater than those of its components. The rejection of these properties constitutes the basics of Compensatory Fuzzy Logic (CFL). The fundamentals of CFL is that an increase or decrease of the truth value of the conjunction or disjunction, as a result of changes in the truth value of one component, can be compensated by an increase or decrease, respectively, of the truth value of other component.

This notion yields a very sensible multi-value logic that maintains the categorical values of the truth values. Also, this capacity makes CFL especially suited for selection problems; yet it is also convenient for ranking, appraising, and classificatory purposes.

Documents

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Compensatory Logic: A Fuzzy Normative Model for Decision Making

Date added:	06/03/2010
Date modified:	06/03/2010
Filesize:	221.24 kB
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This paper proposes an alternative axiomatic multi-valued logic system to overcome some limitations from Fuzzy Logic. The system disregards the classical concepts of norm and co-norm. Existential and universal quantifiers are defined consequently, and propositional bivalent classic calculus is also introduced within the logical structure.

Compensatory Fuzzy Ontology

Date added:	06/10/2010
Date modified:	06/10/2010
Filesize:	Unknown
Downloads:	51

Nowadays, to have relevant information is an important factor that contributes favorably to the decision making process. The usage of ontologies to improve the effectiveness in obtaining information has received special attention from researchers in recent years. However, the conceptual formalism supported by ontologies is not enough to represent the ambiguous information that is commonly founded in many domains of knowledge. An alternative is to incorporate the concepts of compensatory fuzzy logic in order to handle the uncertainty in the data, which take advantage of the benefits it provides for the formal representation of uncertainty. We present in this paper the formal definition of “Compensatory Fuzzy Ontologies” and attempt to bring to light the need for enhanced knowledge representation systems, using the advantages of this approach, which would increase the effectiveness of using knowledge in the field of decision making.