MLN-EIGS：一种用于求解动态交通网络上Stackelberg逃逸拦截博弈的多层网络框架
MLN-EIGS: A multilayer network framework for solving Stackelberg escape interdiction games on dynamic transportation networks
- Kyushu University（九州大学）
- Indian Institute of Technology Kharagpur（印度理工学院卡格布尔分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对动态交通网络上的逃逸拦截问题，提出多层网络框架MLN-EIGS，通过时间扩展网络和近似防御者预言机，高效求解Stackelberg博弈，在保持效用接近精确方法的同时大幅降低计算时间。
AI中文摘要：
在大规模交通网络上，利用有限的警力资源拦截逃逸罪犯是一个具有挑战性的问题，原因在于攻击者移动和防御者部署均具有动态性。本文提出MLN-EIGS，一种基于多层网络的框架，用于求解被建模为Stackelberg安全博弈的动态逃逸拦截问题。构建了一个时间扩展的多层网络，以显式建模交通网络的时间演化以及攻击者和防御者的可行移动。攻击者旨在最大化成功逃逸的概率，而防御者旨在最大化拦截概率。为了高效计算攻击者的最优响应，通过对数变换将概率逃逸问题转化为等价的最短路径问题，从而能够使用Dijkstra算法。由于防御者最优响应问题在计算上难以处理，开发了一个近似防御者预言机，以在多层网络上生成高质量的防御者策略。所提出的MLN-EIGS框架与基于精确混合整数线性规划（MILP）的Stackelberg公式在大型真实交通网络上进行了基准比较。计算实验表明，MLN-EIGS始终能够获得与精确MILP方法非常接近的防御者效用，同时大幅减少计算时间。这些结果表明，所提出的MLN-EIGS框架为大规模动态逃逸拦截问题提供了一种有效、计算高效且可扩展的替代方案，可替代基于精确MILP的Stackelberg优化。
英文摘要：
Interdicting an escaping criminal with limited police resources on large-scale transportation networks is a challenging problem due to the dynamic nature of both attacker movement and defender deployment. This paper proposes \emph{MLN-EIGS}, a multilayer network-based framework for solving dynamic escape interdiction problems formulated as a Stackelberg security game. A time-expanded multilayer network is constructed to explicitly model the temporal evolution of the transportation network and the feasible movements of both the attacker and the defenders. The attacker seeks to maximize the probability of successful escape, while the defenders aim to maximize the probability of interdiction. To efficiently compute the attacker's best response, the probabilistic escape formulation is transformed into an equivalent shortest-path problem through a logarithmic transformation, enabling the use of Dijkstra's algorithm. Since the defender best-response problem is computationally intractable, an approximation defender oracle is developed to generate high-quality defender strategies on the multilayer network. The proposed MLN-EIGS framework is benchmarked against an exact mixed-integer linear programming (MILP)-based Stackelberg formulation on a large real-world transportation network. Computational experiments demonstrate that MLN-EIGS consistently achieves defender utilities that closely match those of the exact MILP approach while substantially reducing computational time. These results demonstrate that the proposed MLN-EIGS framework provides an effective, computationally efficient, and scalable alternative to exact MILP-based Stackelberg optimization for large-scale dynamic escape interdiction problems.
