Arc and path consistence revisited
Artificial Intelligence
Concurrent prolog: collected papers
Concurrent prolog: collected papers
POPL '87 Proceedings of the 14th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
A generic arc-consistency algorithm and its specializations
Artificial Intelligence
Meta-level interpretation of CLP(Lists)
Constraint logic programming
Arc-consistency and arc-consistency again
Artificial Intelligence
Embedding extensional finite sets in CLP
ILPS '93 Proceedings of the 1993 international symposium on Logic programming
ACM Computing Surveys (CSUR) - Special issue: position statements on strategic directions in computing research
Combination of constraint solvers for free and quasi-free structures
Theoretical Computer Science - Special issue: rewriting systems and applications
Using constraint metaknowledge to reduce arc consistency computation
Artificial Intelligence
A Sufficient Condition for Backtrack-Free Search
Journal of the ACM (JACM)
Sets and constraint logic programming
ACM Transactions on Programming Languages and Systems (TOPLAS)
Mixed-initiative, multi-source information assistants
Proceedings of the 10th international conference on World Wide Web
From eager to lazy constrained data acquisition: a general framework
New Generation Computing
Constraint Propagation and Value Acquisition: Why we should do it Interactively
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Least Commitment on Variable Binding in Presence of Incomplete Knowledge
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
A classification and constraint-based framework for configuration
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A CHR-based implementation of known arc-consistency
Theory and Practice of Logic Programming
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A CHR-based implementation of known arc-consistency
Theory and Practice of Logic Programming
Integrating Finite Domain and Set Constraints into a Set-based Constraint Language
Fundamenta Informaticae - Advances in Computational Logic (CIL C08)
A 25-year perspective on logic programming
Programming with partially specified aggregates in Java
Computer Languages, Systems and Structures
Constraint programming on infinite data streams
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Extending the TOY system with the ECLiPSe solver over sets of integers
FLOPS'12 Proceedings of the 11th international conference on Functional and Logic Programming
Integrating Finite Domain and Set Constraints into a Set-based Constraint Language
Fundamenta Informaticae - Advances in Computational Logic (CIL C08)
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Constraint Logic Programming languages on Finite Domains, CLP(FD), provide a declarative framework for Artificial Intelligence problems. However, in many real life cases, domains are not known and must be acquired or computed. In systems that interact with the outer world, domain elements synthesize information on the environment, they are not all known at the beginning of the computation, and must be retrieved through an expensive acquisition process.In this article, we extend the CLP(FD) language by combining it with a new sort (called Incrementally specified Sets, I-Set). In the resulting language, CLP(FD + I-Set), FD variables can be defined on partially or fully unknown domains (I-Set). Domains can be linked each other through relations, and constraints can be imposed on them. We describe a propagation algorithm (called Known Arc Consistency (KAC)) based on known domain elements, and theoretically compare it with arc-consistency.The language can be implemented on top of different CLP systems, thus letting the user exploit different possible semantics for domains (e.g., lists, sets or streams). We state the specifications that the employed system should provide, and we show that two different CLP systems (Conjunto and {log}) can be effectively used.We provide motivating examples and describe promising applications.