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020 _a9783031605994
_9978-3-031-60599-4
024 7 _a10.1007/978-3-031-60599-4
_2doi
050 4 _aQA76.9.M35
072 7 _aUYAM
_2bicssc
072 7 _aCOM014000
_2bisacsh
072 7 _aUYAM
_2thema
245 1 0 _aIntegration of Constraint Programming, Artificial Intelligence, and Operations Research
_h[electronic resource] :
_b21st International Conference, CPAIOR 2024, Uppsala, Sweden, May 28–31, 2024, Proceedings, Part II /
_cedited by Bistra Dilkina.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXIV, 317 p. 79 illus., 62 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
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490 1 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v14743
505 0 _aCore Boosting in SAT-Based Multi-Objective Optimization -- Fair Minimum Representation Clustering -- Proof Logging for the Circuit Constraint -- Probabilistic Lookahead Strong Branching via a Stochastic Abstract Branching Model -- Lookahead, Merge and Reduce for Compiling Relaxed Decision Diagrams for Optimization -- LEO: Learning Efficient Orderings for Multiobjective BDDs -- Minimizing the Cost of Leveraging Influencers in Social Networks: IP and CP Approaches -- Learning Deterministic Surrogates for Robust Convex QCQP -- Strategies for Compressing the Pareto Frontier: Application to Strategic Planning of Hydropower in the Amazon Basin -- Improving Metaheuristic Effciency for Stochastic Optimization Problems by Sequential Predictive Sampling -- SMT-based Repair of Disjunctive Temporal Networks with Uncertainty: Strong and Weak Controllability -- CaVE: A Cone-aligned Approach for Fast Predict-then-optimize with Binary Linear Programs -- A Constraint Programming Approach for Aircraft Disassembly Scheduling -- Optimization Over Trained Neural Networks: Taking a Relaxing Walk -- Learning From Scenarios for Repairable Stochastic Scheduling -- Explainable Algorithm Selection for the Capacitated Lot Sizing Problem -- Efficient Structured Perceptron for NP-hard Combinatorial Optimization Problems -- Robustness Verification in Neural Networks -- An Improved Neuro-Symbolic Architecture to Fine-Tune Generative AI Systems -- Bound Tightening using Rolling-Horizon Decomposition for Neural Network Verification -- Learning Heuristics for Combinatorial Optimization Problems on K-Partite Hypergraphs.
520 _aThis book constitutes the proceedings of the 21st International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2024, held in Uppsala, Sweden, during May 28–31, 2024.The 33 full papers and the 9 short papers presented in the proceedings were carefully reviewed and selected from a total of 104 submissions. The content of the papers focus on new techniques or applications in the area and foster the integration of techniques from different fields dealing with large and complex problems. .
650 0 _aComputer science
_xMathematics.
_964041
650 0 _aArtificial intelligence.
_958441
650 0 _aComputer science.
_958552
650 0 _aComputer networks .
_960157
650 1 4 _aMathematics of Computing.
_966501
650 2 4 _aArtificial Intelligence.
_958441
650 2 4 _aTheory of Computation.
_967552
650 2 4 _aComputer Communication Networks.
_965689
700 1 _aDilkina, Bistra.
_eeditor.
_0(orcid)0000-0002-6784-473X
_1https://orcid.org/0000-0002-6784-473X
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_977553
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031606014
776 0 8 _iPrinted edition:
_z9783031606007
830 0 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v14743
856 4 0 _uhttps://doi.org/10.1007/978-3-031-60599-4
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912 _aZDB-2-SXCS
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