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Solution sampling with random table constraints 具有随机表约束的解采样
IF 1.6 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-06-24 DOI: 10.1007/s10601-022-09329-w
Mathieu Vavrille, C. Truchet, Charles Prud’homme
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引用次数: 2
How constraint programming can help chemists to generate Benzenoid structures and assess the local Aromaticity of Benzenoids 约束规划如何帮助化学家生成苯类化合物结构并评估苯类化合物的局部芳香性
IF 1.6 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-05-28 DOI: 10.1007/s10601-022-09328-x
Yannick Carissan, D. Hagebaum-Reignier, Nicolas Prcovic, C. Terrioux, A. Varet
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引用次数: 1
When bounds consistency implies domain consistency for regular counting constraints 当边界一致性意味着正则计数约束的域一致性时
IF 1.6 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-05-20 DOI: 10.1007/s10601-022-09333-0
B. Martin, J. Pearson
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引用次数: 0
The smallest hard trees 最小的硬树
IF 1.6 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-05-16 DOI: 10.1007/s10601-023-09341-8
M. Bodirsky, Jakub Bulín, Florian Starke, Michael Wernthaler
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引用次数: 0
Learning the travelling salesperson problem requires rethinking generalization 学习旅行推销员问题需要重新思考概括
IF 1.6 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-04-28 DOI: 10.1007/s10601-022-09327-y
Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau, Thomas Laurent

End-to-end training of neural network solvers for graph combinatorial optimization problems such as the Travelling Salesperson Problem (TSP) have seen a surge of interest recently, but remain intractable and inefficient beyond graphs with few hundreds of nodes. While state-of-the-art learning-driven approaches for TSP perform closely to classical solvers when trained on trivially small sizes, they are unable to generalize the learnt policy to larger instances at practical scales. This work presents an end-to-end neural combinatorial optimization pipeline that unifies several recent papers in order to identify the inductive biases, model architectures and learning algorithms that promote generalization to instances larger than those seen in training. Our controlled experiments provide the first principled investigation into such zero-shot generalization, revealing that extrapolating beyond training data requires rethinking the neural combinatorial optimization pipeline, from network layers and learning paradigms to evaluation protocols. Additionally, we analyze recent advances in deep learning for routing problems through the lens of our pipeline and provide new directions to stimulate future research.

图组合优化问题(如旅行销售人员问题(TSP))的神经网络求解器的端到端训练最近引起了人们的极大兴趣,但在只有几百个节点的图之外仍然难以处理且效率低下。虽然最先进的TSP学习驱动方法在非常小的规模上训练时与经典求解器表现接近,但它们无法将学习到的策略推广到实际规模的更大实例。这项工作提出了一个端到端的神经组合优化管道,它结合了最近的几篇论文,以识别归纳偏差、模型架构和学习算法,这些算法可以促进泛化到比训练中看到的更大的实例。我们的对照实验为这种零概率泛化提供了第一个原则性研究,揭示了在训练数据之外的外推需要重新思考神经组合优化管道,从网络层和学习范式到评估协议。此外,我们通过管道的视角分析了深度学习解决路由问题的最新进展,并提供了刺激未来研究的新方向。
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引用次数: 0
A constraint-based approach to learn temporal features on action models from multiple plans 基于约束的多计划动作模型时间特征学习方法
IF 1.6 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-04-01 DOI: 10.1007/s10601-022-09330-3
Antonio Garrido
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引用次数: 0
Concise integer linear programming formulation for clique partitioning problems 团划分问题的简洁整数线性规划公式
IF 1.6 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-04-01 DOI: 10.1007/s10601-022-09326-z
M. Koshimura, Emi Watanabe, Y. Sakurai, M. Yokoo
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引用次数: 2
Complete symmetry breaking constraints for the class of uniquely Hamiltonian graphs 一类唯一Hamilton图的完全对称破缺约束
IF 1.6 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-01-25 DOI: 10.1007/s10601-021-09323-8
Avraham Itzhakov, M. Codish
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引用次数: 0
Boosting isomorphic model filtering with invariants 用不变量增强同构模型滤波
IF 1.6 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-01-21 DOI: 10.1007/s10601-022-09336-x
João Araújo, Choiwah Chow, Mikoláš Janota
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引用次数: 2
Global domain views for expressive and cross-domain constraint programming 用于表达性和跨域约束规划的全局域视图
IF 1.6 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2022-01-11 DOI: 10.1007/s10601-021-09324-7
Dimitri Justeau‐Allaire, Charles Prud’homme
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引用次数: 2
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Constraints
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