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Automatically configuring constraint satisfaction programs: A case study

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Learning Search Control Knowledge
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First-Order Logic
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MACHINE TRANSFORMATION OF ADVICE INTO A HEURISTIC SEARCH PROCEDURE
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inferredcites →https://doi.org/10.1016/0004-3702(92)90010-uopenalex-snapshot
Learning to improve constraint-based scheduling
inferredcites →https://doi.org/10.1016/0004-3702(92)90020-xopenalex-snapshot
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inferredcites →https://doi.org/10.1016/0004-3702(95)00046-1openalex-snapshot
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inferredcites →https://doi.org/10.1016/0022-2496(83)90037-8openalex-snapshot
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Constraint Satisfaction with a Multi-Dimensional Domain
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Explanation-Based Generalization: A Unifying View
inferredcites →https://doi.org/10.1016/b978-1-55860-200-7.50029-5openalex-snapshot
Learning Variable Descriptors for Applying Heuristics Across CSP Problems
inferredcites →https://doi.org/10.1016/b978-1-55860-200-7.50104-5openalex-snapshot
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inferredcites →https://doi.org/10.1016/c2013-0-07691-8openalex-snapshot
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inferredcites →https://doi.org/10.1023/a:1022691120807openalex-snapshot
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inferredcites →https://doi.org/10.1109/kbse.1992.252912openalex-snapshot
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Abstraction via approximate symmetry
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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Bibliography
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Constraint Satisfaction
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Constraint Programming
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Using CBR to Select Solution Strategies in Constraint Programming
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Evolving Variable-Ordering Heuristics for Constrained Optimisation
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A Meta-heuristic for Subset Problems
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Towards Inferring Labelling Heuristics for CSP Application Domains
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Collaborative Learning for Constraint Solving
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Achievements and Prospects of Program Synthesis
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The Adaptive Constraint Engine
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np-spec: An Executable Specification Language for Solving All Problems in NP
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A General Model for Automated Algorithm Design
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Fundamental Approaches to Software Engineering
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Construction of Verifier Combinations Based on Off-the-Shelf Verifiers
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Location, Size, and Capacity
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Instance-Specific Algorithm Configuration
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Introduction
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Improving Cluster-Based Algorithm Selection
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Conclusion
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Related Work
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Instance-Specific Algorithm Configuration
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Training Parameterized Solvers
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ISAC for Algorithm Selection
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Training Parallel Solvers
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Dynamic Approach for Switching Heuristics
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Evolving Instance-Specific Algorithm Configuration
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Algorithm Selection for Combinatorial Search Problems: A Survey
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Simple Rules for Low-Knowledge Algorithm Selection
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A Statistical Approach for Algorithm Selection
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Introducing esra, a Relational Language for Modelling Combinatorial Problems
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Synthesis of Programs in Computational Logic
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A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms
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Ensemble Classification for Constraint Solver Configuration
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Optimisation and Generalisation: Footprints in Instance Space
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Construction of verifier combinations from off-the-shelf components
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