Logical analysis of fuzzy constraint satisfaction problems

João Moura Pires; Henri Prade

doi:10.1109/FUZZY.1998.687603

Logical analysis of fuzzy constraint satisfaction problems

João Moura Pires, Henri Prade

DI - Departamento de Informática

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Fuzzy sets naturally model elastic constraints. Fuzzy constraints satisfaction problem (FCSP) enable the introduction of different kinds of flexibility. Levels of priority can be attached to constraints, and satisfaction levels can be fuzzily thresholded. Fuzzy constraints are aggregated by min operation into a fuzzy set membership function to be maximized (discrimin and leximin refinements of the min ordering can be also used in this maximization). This representation framework, originally expressed in terms of membership functions, is equivalently translated into a set of prioritized crisp constraints, in this paper. We take advantage of this representation for modelling aggregations different from min, expressing either reinforcement and compensation. This offers a logical understanding of fuzzy constraints. In relation to this new representation scheme, computational aspects are briefly exemplified and discussed.

Original language	English
Title of host publication	1998 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AT THE IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE - PROCEEDINGS
Publisher	IEEE
Pages	857-862
Number of pages	6
Volume	1
ISBN (Print)	0-7803-4863-X
DOIs	https://doi.org/10.1109/FUZZY.1998.687603
Publication status	Published - 1998
Event	IEEE International Conference on Fuzzy Systems at the World Congress on Computational Intelligence (WCCI 98) - Anchorage, United States Duration: 4 May 1998 → 9 May 1998

Conference

Conference	IEEE International Conference on Fuzzy Systems at the World Congress on Computational Intelligence (WCCI 98)
Country	United States
City	Anchorage
Period	4/05/98 → 9/05/98

Access to Document

10.1109/FUZZY.1998.687603

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title = "Logical analysis of fuzzy constraint satisfaction problems",

abstract = "Fuzzy sets naturally model elastic constraints. Fuzzy constraints satisfaction problem (FCSP) enable the introduction of different kinds of flexibility. Levels of priority can be attached to constraints, and satisfaction levels can be fuzzily thresholded. Fuzzy constraints are aggregated by min operation into a fuzzy set membership function to be maximized (discrimin and leximin refinements of the min ordering can be also used in this maximization). This representation framework, originally expressed in terms of membership functions, is equivalently translated into a set of prioritized crisp constraints, in this paper. We take advantage of this representation for modelling aggregations different from min, expressing either reinforcement and compensation. This offers a logical understanding of fuzzy constraints. In relation to this new representation scheme, computational aspects are briefly exemplified and discussed.",

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note = "IEEE International Conference on Fuzzy Systems at the World Congress on Computational Intelligence (WCCI 98) ; Conference date: 04-05-1998 Through 09-05-1998",

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Pires, JM & Prade, H 1998, Logical analysis of fuzzy constraint satisfaction problems. in 1998 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AT THE IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE - PROCEEDINGS. vol. 1, IEEE, pp. 857-862, IEEE International Conference on Fuzzy Systems at the World Congress on Computational Intelligence (WCCI 98), Anchorage, United States, 4/05/98. https://doi.org/10.1109/FUZZY.1998.687603

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AU - Prade, Henri

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AB - Fuzzy sets naturally model elastic constraints. Fuzzy constraints satisfaction problem (FCSP) enable the introduction of different kinds of flexibility. Levels of priority can be attached to constraints, and satisfaction levels can be fuzzily thresholded. Fuzzy constraints are aggregated by min operation into a fuzzy set membership function to be maximized (discrimin and leximin refinements of the min ordering can be also used in this maximization). This representation framework, originally expressed in terms of membership functions, is equivalently translated into a set of prioritized crisp constraints, in this paper. We take advantage of this representation for modelling aggregations different from min, expressing either reinforcement and compensation. This offers a logical understanding of fuzzy constraints. In relation to this new representation scheme, computational aspects are briefly exemplified and discussed.

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