Constraint satisfaction in logic programming
Constraint satisfaction in logic programming
The CLP( R ) language and system
ACM Transactions on Programming Languages and Systems (TOPLAS)
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ACM Transactions on Computer Systems (TOCS)
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Journal of the ACM (JACM)
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ACM Transactions on Programming Languages and Systems (TOPLAS)
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IEEE Transactions on Knowledge and Data Engineering
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CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
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ATAL '97 Proceedings of the 4th International Workshop on Intelligent Agents IV, Agent Theories, Architectures, and Languages
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LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
Evolution of the GPGP Domain-Independent Coordination Framework
Evolution of the GPGP Domain-Independent Coordination Framework
Parallel and Distributed Finite Constraint Satisfaction: Complexity, Algorithms and Experiments
Parallel and Distributed Finite Constraint Satisfaction: Complexity, Algorithms and Experiments
Fuzzy Cognitive Layer in RoboCupSoccer
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Information Sciences: an International Journal
Fuzzy agent-based approach for consensual design synthesis in product configuration
Integrated Computer-Aided Engineering
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Truth values associated to fuzzy variables can be represented in an ordeal of different flavors, such as real numbers, percentiles, intervals, unions of intervals, and continuous or discrete functions on different domains. Many of the most interesting fuzzy problems deal with a discrete range of truth values. In this work we represent these ranges using Constraint Logic Programming over Finite Domains (CLP($\mathcal{FD}$)). This allows to produce finite enumerations of constructive answers instead of complicated, hardly self-explanatory, constraints expressions. Another advantage of representing fuzzy models through finite domains is that some of the existing techniques and algorithms of the field of distributed constraint programming can be borrowed. In this paper we exploit these considerations in order to create a new generation of collaborative fuzzy agents in a distributed environment.