VISER：一种适用于信息不对称博弈的易处理解概念
VISER: A Tractable Solution Concept for Games with Information Asymmetry
AI总结：
针对信息不对称博弈中传统解概念失效的问题，提出新型解概念VISER，可通过线性规划在多项式时间内独立计算各参与者策略，并扩展至马尔可夫博弈场景，助力安全防御与攻击刻画。
摘要：
许多现实世界的博弈存在信息不对称问题：一方参与者仅知晓自身收益，而另一方掌握博弈的全部信息。这类场景的典型例子包括安全博弈这一关键领域，以及对抗式多智能体强化学习。信息不对称使得强斯塔克尔伯格均衡（SSE）、鲁棒优化均衡（ROE）等传统解概念失效。我们提出了一种名为VISER（受害者安全、剥削者最优响应，Victim Is Secure, Exploiter best-Responds）的新型解概念。VISER可让外部观察者预测此类博弈的结果。具体而言，在安全应用场景中，VISER既能帮助受害者更好地开展防御，也能刻画攻击者可实施的最具破坏性的攻击。我们证明，每个参与者的VISER策略均可通过线性规划（LP）在多项式时间内独立计算得到。我们还将VISER扩展至适用于马尔可夫博弈的马尔可夫完美版本，该版本可通过一系列线性规划高效求解。
英文摘要：
Many real-world games suffer from information asymmetry: one player is only aware of their own payoffs while the other player has the full game information. Examples include the critical domain of security games and adversarial multi-agent reinforcement learning. Information asymmetry renders traditional solution concepts such as Strong Stackelberg Equilibrium (SSE) and Robust-Optimization Equilibrium (ROE) inoperative. We propose a novel solution concept called VISER (Victim Is Secure, Exploiter best-Responds). VISER enables an external observer to predict the outcome of such games. In particular, for security applications, VISER allows the victim to better defend itself while characterizing the most damaging attacks available to the attacker. We show that each player's VISER strategy can be computed independently in polynomial time using linear programming (LP). We also extend VISER to its Markov-perfect counterpart for Markov games, which can be solved efficiently using a series of LPs.
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。
