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A defender-attacker model and algorithm for maximizing weighted expected hitting time with application to conservation planning

机译:用于最大化加权预期击中时间的防御者-攻击者模型和算法,应用于保护规划

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This article studies an interdiction problem in which two agents with opposed interests, a defender and an attacker, interact in a system governed by an absorbing discrete-time Markov chain.The defender protects a subset of transient states, whereas the attacker targets a subset of the unprotected states. By changing some of the transition probabilities related to the attacked states, the attacker seeks to minimize the Weighted Expected Hitting Time (WEHT). The defender seeks to maximize the attacker's minimum possible objective, mitigating the worst-case WEHT. Many applications can be represented by this problem; this article focuses on conservation planning. We present a defender-attacker model and algorithm for maximizing the minimum WEHT. As WEHT is not generally a convex function of the attacker's decisions, we examine large-scale integer programming formulations and first-order approximation methods for its solution. We also develop an algorithm for solving the defender's problem via mixed-integer programming methods augmented by supervalid inequalities. The efficacy of the proposed solution methods is then evaluated using data from a conservation case study, along with an array of randomly generated instances.
机译:本文研究了一个拦截问题,在该问题中,具有相对利益的两个主体(防御者和攻击者)在由吸收性离散时间马尔可夫链控制的系统中进行交互。防御者保护瞬态的子集,而攻击者针对瞬态的子集未受保护的国家。通过更改与被攻击状态有关的某些过渡概率,攻击者试图最小化加权预期命中时间(WEHT)。防御者试图最大化攻击者的最低可能目标,从而减轻最坏情况下的WEHT。这个问题可以代表许多应用。本文重点介绍保护规划。我们提出了一种防御者-攻击者模型和算法,用于最大化最小WEHT。由于WEHT通常不是攻击者决策的凸函数,因此我们研究了大规模整数规划公式和其解的一阶逼近方法。我们还开发了一种算法,该算法通过使用超有效不等式增强的混合整数编程方法来解决防御者的问题。然后,使用保护案例研究中的数据以及一系列随机生成的实例对所提出的解决方案方法的有效性进行评估。

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