Possibilistic constraint satisfaction problems or “how to handle soft constraints?”
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
A Reactive Approach for Solving Constraint Satisfaction Problems
ATAL '98 Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages
Relaxations of semiring constraint satisfaction problems
Information Processing Letters
Large scale evolutionary optimization using cooperative coevolution
Information Sciences: an International Journal
Preference-based search with adaptive recommendations
AI Communications - Recommender Systems
Local search: A guide for the information retrieval practitioner
Information Processing and Management: an International Journal
AND/OR Branch-and-Bound search for combinatorial optimization in graphical models
Artificial Intelligence
Preference-based search using example-critiquing with suggestions
Journal of Artificial Intelligence Research
An erosion model for evaluating regional land-use scenarios
Environmental Modelling & Software
Engineering Applications of Artificial Intelligence
Multiscale Optimization for the Management of Runoff Risks in Agricultural Watersheds
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Geographic applications are often over-constrained because of the stakeholders' multiple requirements and the various spatial, alphanumeric and temporal constraints to be satisfied. In most cases, solving over-constrained problems is based on the relaxation of some constraints according to values of preferences. This article proposes the modelling and the management of constraints in order to provide a framework to integrate stakeholders in the expression and the relaxation of their constraints. Three families of constraints are defined: static vs. dynamic, intra-entity vs. inter-entities and intra-instance vs. inter-instances. Constraints are modelled from two points of view: system with the complexity in time of the different involved operators and user with stakeholders' preferences. The methodology of constraints relaxation is based on primitive, complex and derived operations. These operations allow a modification of the constraints in order to provide a relevant solution to a simulation. The developed system was applied to reduce the streaming/floods risks in the territory of Pays de Caux (Seine Maritime, France).