English(EN) Decision-Focused Learning in Network Interdiction Games

新的对抗性DFL方法改进了网络拦截博弈学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-11 04:00

研究人员开发了一种名为对抗性DFL（A-DFL）的新方法，以解决决策导向学习（DFL）应用于网络拦截博弈时出现的根本性故障。在这些博弈中，拦截者加强网络弧线，而规避者使用机器学习预测器寻找最短路径，这常常导致DFL的训练目标允许成本估计器在拦截下表现不佳。A-DFL用被拦截的场景替换名义训练样本来克服这个问题，恢复了DFL的优势，并实现了有效的端到端优化。 AI

影响这项研究可能带来更强大的AI系统，用于在对抗性环境中进行战略决策。

排序理由该集群包含一篇详细介绍新机器学习方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Luca M. Hartmann, Parinaz Naghizadeh · 2026-08-11 04:00

网络拦截博弈中的决策导向学习

arXiv:2608.09036v1 Announce Type: cross Abstract: We study decision-focused learning (DFL) in shortest-path network interdiction (SPNI) games, a Stackelberg game where an interdictor (leader) strengthens the networks' arcs against attacks, while an evader (follower) who is uncert…

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网络拦截博弈中的决策导向学习

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