Competitive Safety Analysis：多Agent系统中的鲁棒决策
Competitive Safety Analysis: Robust Decision-Making in Multi-Agent Systems
摘要：
人工智能中的许多工作都涉及在给定（已知或未知）环境中选择适当的动作。然而，在面对其他代理人时，如何选择适当的行动是相当不清楚的。人工智能领域的大多数工作都采用经典的博弈论均衡分析来预测智能体在这种环境下的行为。然而，这种方法并不能为我们的代理商提供任何保证。本文介绍了竞争安全分析。这种方法弥合了所需的规范性人工智能方法与均衡分析之间的差距，在规范性人工智能方法中，应该选择一种策略以保证所需的回报。我们表明，一个安全级别的战略是能够保证在纳什均衡中获得的价值，在几个经典的计算机科学设置。然后，我们讨论的竞争安全策略的概念，并说明其使用在一个分散的负载均衡设置，典型的网络问题。特别是，我们表明，当我们有许多代理人，它是可能的，以保证预期的回报，这是一个因素的8/9的回报中获得的纳什均衡。我们讨论的竞争安全分析分散负载均衡进一步发展，以处理许多通信链路和任意的速度。最后，我们讨论了上述概念的贝叶斯博弈的扩展，并说明了它们的使用在一个基本的拍卖设置。
英文摘要：
Much work in AI deals with the selection of proper actions in a given (known or unknown) environment. However, the way to select a proper action when facing other agents is quite unclear. Most work in AI adopts classical game-theoretic equilibrium analysis to predict agent behavior in such settings. This approach however does not provide us with any guarantee for the agent. In this paper we introduce competitive safety analysis. This approach bridges the gap between the desired normative AI approach, where a strategy should be selected in order to guarantee a desired payoff, and equilibrium analysis. We show that a safety level strategy is able to guarantee the value obtained in a Nash equilibrium, in several classical computer science settings. Then, we discuss the concept of competitive safety strategies, and illustrate its use in a decentralized load balancing setting, typical to network problems. In particular, we show that when we have many agents, it is possible to guarantee an expected payoff which is a factor of 8/9 of the payoff obtained in a Nash equilibrium. Our discussion of competitive safety analysis for decentralized load balancing is further developed to deal with many communication links and arbitrary speeds. Finally, we discuss the extension of the above concepts to Bayesian games, and illustrate their use in a basic auctions setup.
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。
