Constraint satisfaction using soft quantifiers | Zendy

Yager Ronald R. | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Premium

Constraint satisfaction using soft quantifiers

Author(s) -

Yager Ronald R.

Publication year - 2004

Publication title -

intelligent systems in accounting, finance and management

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.846

H-Index - 11

eISSN - 2160-0074

pISSN - 1550-1949

DOI - 10.1002/isaf.250

Subject(s) - constraint (computer aided design) , softening , constraint satisfaction problem , operator (biology) , aggregate (composite) , fuzzy logic , process (computing) , local consistency , computer science , constraint satisfaction , mathematics , artificial intelligence , statistics , programming language , biochemistry , chemistry , geometry , materials science , repressor , probabilistic logic , transcription factor , composite material , gene

Abstract Fuzzy sets and other methods have been used to model a softening of constraints in constraint propagation (CP) problems. Here, we suggest an approach to the softening of the CP problem at the meta level, in the process used to aggregate the satisfactions to the individual constraints. We discuss the possibility of using soft quantiers such as ‘most’ to guide the process of aggregating the satisfactions to the individual constraints. Use is made of the ability to represent these soft quantiers by fuzzy sets and the ability to implement their authorized aggregation by the ordered weighted averaging operator. Copyright © 2004 John Wiley & Sons, Ltd.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here

Accelerating Research