Principles and Practice of Constraint Programming

25th International Conference, CP 2019, Stamford, CT, USA, September 30 – October 4, 2019, Proceedings

AvThomas Schiex,Simon de Givry

Häftad, Engelska, 2019

Del 11802 i serien Lecture Notes in Computer Science

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Beskrivning

This book constitutes the proceedings of the 25th International Conference on Principles and Practice of Constraint Programming, CP 2019, held in Stamford, CT, USA, France, in September/October 2019.The 44 full papers presented in this volume were carefully reviewed and selected from 118 submissions. multi-agent and parallel CP track;

Produktinformation

Utgivningsdatum:2019-08-30
Mått:155 x 235 x 44 mm
Vikt:1 212 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:788
Förlag:Springer Nature Switzerland AG
ISBN:9783030300470

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Programvaruutveckling inom Data och IT
Artificiell intelligens inom Data och IT

Innehållsförteckning

Technical Track.- Instance Generation via Generator Instances.- Automatic Detection of At-Most-One and Exactly-One Relations for Improved SAT Encodings of Pseudo-Boolean Constraints.- Exploring Declarative Local-Search Neighbourhoods with Constraint Programming.- Vehicle routing by learning from historical solutions.- On Symbolic Approaches for Computing the Matrix Permanent.- Towards the Characterization of Max-Resolution Transformations of UCSs by UP-Resilience.- Logic-Based Benders Decomposition for Super Solutions: an Application to the Kidney Exchange Problem.- Exploiting Glue Clauses to Design Effective CDCL Branching Heuristics.- Industrial Size Job-Shop Scheduling tackled by Present-Day CP Solvers.- Dual Hashing-based Algorithms for Discrete Integration.- Techniques Inspired by Local Search for Incomplete MaxSAT and the Linear Algorithm: Varying Resolution and Solution-Guided Search.- A Join-Based Hybrid Parameter for Constraint Satisfaction.- An Incremental SAT-BasedApproach to the Graph Colouring Problem.- Constraint-based Techniques in Stochastic Local Search MaxSAT Solving.- Trimming Graphs Using Clausal Proof Optimization.- Improved Job Sequencing Bounds from Decision Diagrams.- Integration of structural constraints into TSP models.- Representing fitness landscapes by valued constraints to understand the complexity of local search.- Estimating the Number of Solutions of Cardinality Constraints through range and roots Decomposition.- Understanding the Empirical Hardness of Random Optimisation Problems.- Guarded Constraint Models Define Treewidth Preserving Reductions.- Automatic Streamlining for Constrained Optimisation.- Compiling Conditional Constraints.- Training Binarized Neural Networks using MIP and CP.- Application Track.- Models for Radiation Therapy Patient Scheduling.- Constraint Programming-based Job Dispatching for Modern HPC Applications.- Scheduling of Mobile Robots using Constraint Programming.- Decomposition and Cut Generation Strategies for Solving Multi-Robot Deployment Problems.- Multi-agent and Parallel CP Track.- An Improved GPU-based SAT Model Counter.- Reducing Bias in Preference Aggregation for Multiagent Soft Constraint Problems.- Testing and Verification Track.- A Cube Distribution Approach to QBF Solving and Certificate Minimization.- Functional Synthesis with Examples.- SolverCheck: Declarative Testing of Constraints.- Encodings for Enumeration-Based Program Synthesis.- Lemma Synthesis for Automating Induction over Algebraic Data Types.- CP and Data Science Track.- Modeling Pattern Set Mining using Boolean Circuits.- Differential Privacy of Hierarchical Census Data: An Optimization Approach.- Generic Constraint-based Block Modeling using Constraint Programming.- Reward Potentials for Planning with Learned Neural Network Transition Models.- Exploiting Counterfactuals for Scalable Stochastic Optimization.- Structure-driven Multiple Constraint Acquisition.- Computational Sustainability Track.- Towards robust scenarios of spatio-temporal renewable energy planning: A GIS-RO approach.- Peak-hour Rail Demand Shifting with Discrete Optimisation.- CP and Life Sciences Track.- Functional significance checking in noisy gene regulatory networks.