Principles and Practice of Constraint Programming - Info and Reading Options

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Intro -- Preface -- Tutorials and Workshops -- Organization -- Invited Talks -- Using Constraints in Machine Learning -- Discrete Optimization and Machine Learning for Sustainability -- Verification and Explanation of Deep Neural Networks -- AI and Robust Optimization for Social Good -- Contents -- Technical Track -- Instance Generation via Generator Instances -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Methodology -- 4.1 Rewriting Rules -- 4.2 Correctness of Instance Generation via Generator Instances -- 4.3 Tuning Instance Difficulty -- 4.4 Problem Classes -- 4.5 Experimental Setup -- 5 Results and Analysis -- 6 Conclusions and Future Work -- References -- Automatic Detection of At-Most-One and Exactly-One Relations for Improved SAT Encodings of Pseudo-Boolean Constraints -- 1 Introduction -- 2 Preliminaries -- 3 Background: AMO and ALO Detection -- 4 AMO and EO Relations in Savile Row -- 4.1 Mutex Inference -- 4.2 Normalisation -- 4.3 AMO and EO Detection -- 4.4 Reformulation Example -- 5 Experimental Evaluation -- 5.1 Combinatorial Auctions -- 5.2 MRCPSP -- 5.3 NSP -- 5.4 MMKP -- 5.5 Experimental Results -- 6 Conclusion and Future Work -- References -- Exploring Declarative Local-Search Neighbourhoods with Constraint Programming -- 1 Introduction -- 2 Background -- 2.1 MiniZinc and Declarative Neighbourhoods -- 2.2 Local Search -- 3 Encoding a Declarative Neighbourhood as a CP Model -- 3.1 Encoding the Current and Next Valuations -- 3.2 Encoding a Move -- 3.3 The Neighbourhood Model and Neighbourhood Exploration -- 4 Implementing a Local-Search Solver Using a CP Solver -- 4.1 Implementation of the WRITES Global Constraint -- 4.2 Constraint Softening Scheme -- 4.3 Control Flow -- 5 Experimental Evaluation -- 6 Conclusion, Related Work, and Future Work -- References -- Vehicle Routing by Learning from Historical Solutions

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