Titlebook: Integration of Constraint Programming, Artificial Intelligence, and Operations Research; 19th International C Pierre Schaus Conference proc

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書目名稱Integration of Constraint Programming, Artificial Intelligence, and Operations Research影響因子(影響力)




書目名稱Integration of Constraint Programming, Artificial Intelligence, and Operations Research影響因子(影響力)學(xué)科排名




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書目名稱Integration of Constraint Programming, Artificial Intelligence, and Operations Research被引頻次




書目名稱Integration of Constraint Programming, Artificial Intelligence, and Operations Research被引頻次學(xué)科排名




書目名稱Integration of Constraint Programming, Artificial Intelligence, and Operations Research年度引用




書目名稱Integration of Constraint Programming, Artificial Intelligence, and Operations Research年度引用學(xué)科排名




書目名稱Integration of Constraint Programming, Artificial Intelligence, and Operations Research讀者反饋




書目名稱Integration of Constraint Programming, Artificial Intelligence, and Operations Research讀者反饋學(xué)科排名

Allodynia

0302-9743 ed and selected from a total of 60 submissions. The conference program included a Master Class on the topic "Bridging the Gap between Machine Learning and Optimization”..978-3-031-08010-4978-3-031-08011-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Conference proceedings 2022Operations Research, CPAIOR 2022, which was held in Los Angeles, CA, USA, in June 2022.The 28 regular papers presented were carefully reviewed and selected from a total of 60 submissions. The conference program included a Master Class on the topic "Bridging the Gap between Machine Learning and Optim

myocardium

,Leveraging Integer Linear Programming to?Learn Optimal Fair Rule Lists,iciently before proposing an Integer Linear Programming method, leveraging accuracy, sparsity and fairness jointly for better pruning. Our thorough experiments show clear benefits of our approach regarding the exploration of the search space.

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legislate

Stochastic Decision Diagrams,optimal solution. This results in a general and completely novel method for obtaining optimization bounds for stochastic dynamic programming, and the only method that can be applied to the original state space. We report computational experience on stochastic maximum clique (equivalently, maximum independent set) problem instances.

chiropractor

Munificent

,Learning a?Propagation Complete Formula, We have implemented both approaches and compared them experimentally. Babka et al. (2013) showed that checking if a CNF formula admits an empowering implicate is an NP-complete problem. We propose a particular CNF encoding which allows us to use a SAT solver to check propagation completeness, or to find an empowering implicate.

craven

,Dealing with?the?Product Constraint,. We propose and compare different representations allowing to compute the set of solutions of this problem exactly or up to a certain precision. We also give an efficient method to represent that constraint by a Multi-valued Decision Diagram (MDD) in order to combine this constraint with some others MDDs.