Authors:
Eric Monfroy
1
;
Frédéric Saubion
2
;
Broderick Crawford
3
and
Carlos Castro
4
Affiliations:
1
University of Nantes, France
;
2
University of Angers, France
;
3
Pontificia Universidad Católica de Valparaíso, Chile
;
4
Universidad Técnica Federico Santa María, Chile
Keyword(s):
Constraint Satisfaction Problems (CSP), Local Search, Constraint Solving.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Enterprise Information Systems
;
Evolutionary Programming
;
Strategic Decision Support Systems
Abstract:
Constraint Satisfaction Problems (CSP) provide a general framework for modeling many practical applications (planning, scheduling, time tabling, . . . ). CSPs can be solved with complete methods (e.g., constraint propagation), or incomplete methods (e.g., local search). Although there are some frameworks to formalize constraint propagation, there are only few studies of theoretical frameworks for local search. Here we are concerned with the design and use of a generic framework to model local search as the computation of a fixed point of functions.