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Titlebook: Integration of Constraint Programming, Artificial Intelligence, and Operations Research; 18th International C Peter J. Stuckey Conference p

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發(fā)表于 2025-3-21 18:15:28 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Integration of Constraint Programming, Artificial Intelligence, and Operations Research
副標題18th International C
編輯Peter J. Stuckey
視頻videohttp://file.papertrans.cn/469/468832/468832.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Integration of Constraint Programming, Artificial Intelligence, and Operations Research; 18th International C Peter J. Stuckey Conference p
描述This volume LNCS 12735 constitutes the papers of the 18th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2021, which was held in Vienna, Austria, in 2021. Due to the COVID-19 pandemic the conference was held online.?The 30 regular papers presented were carefully reviewed and selected from a total of 75 submissions. The conference program included a Master Class on the topic "Explanation and Verification of Machine Learning Models"..
出版日期Conference proceedings 2021
關(guān)鍵詞artificial intelligence; communication systems; computer hardware; computer networks; computer programmi
版次1
doihttps://doi.org/10.1007/978-3-030-78230-6
isbn_softcover978-3-030-78229-0
isbn_ebook978-3-030-78230-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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A Computational Study of Constraint Programming Approaches for Resource-Constrained Project Schedulduling problems where the duration of activities can be reduced when scheduled after certain other activities that allow for learning relevant skills. Since per-period availabilities of renewable resources are limited and precedence requirements have to be respected, the resulting optimization probl
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Strengthening of Feasibility Cuts in Logic-Based Benders Decomposition,ack information. Therefore, an important acceleration technique in LBBD is to strengthen feasibility cuts by reducing their sizes. This is typically done by solving additional subproblems to evaluate potential cuts. In this paper, we study three cut-strengthening algorithms that differ in the comput
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A,-Based Compilation of Relaxed Decision Diagrams for the Longest Common Subsequence Problem,relies on the compilation of a relaxed multi-valued decision diagram (MDD) in a special way that is based on the principles of A. search. An extensive experimental evaluation on several standard LCS benchmark instance sets shows that the novel construction algorithm clearly outperforms a traditional
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Partitioning Students into Cohorts During COVID-19,s. One of the most effective measures to reduce virus transmission is partitioning students into discrete cohorts..In primary and middle schools, it is easy to create these cohorts (also known as “l(fā)earning groups”), since students in each grade take the same set of required courses. However, in high
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Heavy-Tails and Randomized Restarting Beam Search in Goal-Oriented Neural Sequence Decoding,oblems. In these scenarios, the beam search algorithm is typically used to produce a set of high-likelihood candidate sequences that are evaluated to determine if they satisfy the goal criteria. If none of the candidates satisfy the criteria, the beam search can be restarted with a larger beam size
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