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XCSP3: An Integrated Format for Benchmarking Combinatorial Constrained Problems

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arxiv 1611.03398 v5 pith:LW3WXH67 submitted 2016-11-10 cs.AI

classification cs.AI
keywords formatxcsp3combinatorialconstrainedconstraintsinstancesconstraintdeveloped
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We propose a major revision of the format XCSP 2.1, called XCSP3, to build integrated representations of combinatorial constrained problems. This new format is able to deal with mono/multi optimization, many types of variables, cost functions, reification, views, annotations, variable quantification, distributed, probabilistic and qualitative reasoning. The new format is made compact, highly readable, and rather easy to parse. Interestingly, it captures the structure of the problem models, through the possibilities of declaring arrays of variables, and identifying syntactic and semantic groups of constraints. The number of constraints is kept under control by introducing a limited set of basic constraint forms, and producing almost automatically some of their variations through lifting, restriction, sliding, logical combination and relaxation mechanisms. As a result, XCSP3 encompasses practically all constraints that can be found in major constraint solvers developed by the CP community. A website, which is developed conjointly with the format, contains many models and series of instances. The user can make sophisticated queries for selecting instances from very precise criteria. The objective of XCSP3 is to ease the effort required to test and compare different algorithms by providing a common test-bed of combinatorial constrained instances.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 16 citations worldwide. Full citation record

  1. CP-Model-Zoo: A Natural Language Query System for Constraint Programming Models

    cs.AI 2025-09 conditional novelty 5.0 of 10

    CP-Model-Zoo retrieves the most relevant MiniZinc model from a pool of 67 problems using text embeddings, with mean reciprocal rank between 0.87 and 1.00 in the paper's experiments.

  2. Proceedings of the 2024 XCSP3 Competition

    cs.AI 2024-11 conditional novelty 4.0 of 10

    The 2024 XCSP3 competition proceedings document the benchmark suite, solver descriptions, and final rankings across six constraint-solving tracks.

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