Authors:
Tenda Okimoto
1
;
Tony Ribeiro
2
;
Maxime Clement
3
and
Katsumi Inoue
4
Affiliations:
1
Transdisciplinary Research Integration Center and National Institute of Informatics, Japan
;
2
The Graduate University for Advanced Studies, Japan
;
3
Pierre and Marie Curie University, France
;
4
National Institute of Informatics, Japan
Keyword(s):
Dynamic Multi-objective Weighted CSP, (l, s)-Pareto Solution.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Constraint Satisfaction
;
Symbolic Systems
Abstract:
A Constraint Satisfaction Problem (CSP) is a fundamental problem that can formalize various applications related to Artificial Intelligence problems. A Weighted Constraint Satisfaction Problem (WCSP) is a CSP where constraints can be violated, and the aim of this problem is to find an assignment that minimizes the sum of weights of the violated constraints. Most researches have focused on developing algorithms for solving static mono-objective problems. However, many real world satisfaction/optimization problems involve multiple criteria that should be considered separately and satisfied/optimized simultaneously. Additionally, they are often dynamic, i.e., the problem hanges at runtime. In this paper, we introduce a Multi-Objective WCSP (MO-WCSP) and develop a novel MO-WCSP algorithm called Multi-Objective Branch and Bound (MO-BnB), which is based on a new solution criterion called (l, s)-Pareto solution. Furthermore, we first formalize a DynamicMO-WCSP (DMO-WCSP). As an initial step
towards developing an algorithm for solving a DMO-WCSP, we focus on the change of weights of constraints and develop the first algorithm called Dynamic Multi-Objective Branch and Bound (DMO-BnB) for solving a DMO-WCSPs, which is based on MO-BnB. Finally, we provide the complexity of our algorithms and evaluate DMO-BnB with different problem settings.
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