计算最大破坏攻击的最佳对策
Computing a Best Response against a Maximum Disruption Attack
摘要：
受战略网络设计和防御或免疫至关重要的场景的启发，Goyal等人[3]定义了一种新的具有攻击和免疫的网络形成游戏。作者表明，尽管存在攻击，游戏具有高社会福利属性，即使平衡网络可以包含循环，边缘的数量是强有界的。随后，Friedrich等人。[10]提供了一个多项式时间算法，用于计算最大屠杀对手（试图杀死尽可能多的节点）和随机攻击对手的最佳响应策略，但他们留下了最大破坏对手的问题。这个对手攻击的脆弱地区，使攻击后的社会福利最小化。在本文中，我们解决我们的努力，这个问题。我们可以证明，当由给定策略产生的初始网络是连通的时，给定参与人u和除u之外的所有参与人的策略，计算最佳对策策略是多项式时间可解的。我们的算法是基于一个动态规划，并有一些回忆背包问题，虽然是相当复杂和涉及。
英文摘要：
Inspired by scenarios where the strategic network design and defense or immunisation are of the central importance, Goyal et al. [3] defined a new Network Formation Game with Attack and Immunisation. The authors showed that despite the presence of attacks, the game has high social welfare properties and even though the equilibrium networks can contain cycles, the number of edges is strongly bounded. Subsequently, Friedrich et al. [10] provided a polynomial time algorithm for computing a best response strategy for the maximum carnage adversary which tries to kill as many nodes as possible, and for the random attack adversary, but they left open the problem for the case of maximum disruption adversary. This adversary attacks the vulnerable region that minimises the post-attack social welfare. In this paper we address our efforts to this question. We can show that computing a best response strategy given a player u and the strategies of all players but u, is polynomial time solvable when the initial network resulting from the given strategies is connected. Our algorithm is based on a dynamic programming and has some reminiscence to the knapsack-problem, although is considerably more complex and involved.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
