CAREER: Robust Strategic Reasoning for Multi-Agent Systems

计算科学中的许多重要决策问题涉及多智能体系统(MAS)，在MAS中，多个决策者必须从一组备选方案中选择行动策略。(一个例子是选择安全策略-例如由警察-来保护关键基础设施免受攻击者的攻击。)这个职业奖励项目开发了分析MAS的方法，以选择工程师可以遵循的战略，从而产生理想的结果。推动项目的核心租户是，一个好的战略应该是健壮的，即使结果或其他代理将如何选择他们的战略存在不确定性，它也会继续表现良好。该项目的一个重要组成部分是一个多功能网络平台，支持有关游戏代理的研究、实验和教育。PI将设计使用该平台的课程模块，让本科生参与设计游戏代理，提供一种令人兴奋的方式来练习基本编程，发展批判性思维和数据分析技能。由学生设计的代理也在研究中发挥重要作用；它们将提供一个多样化的策略库，以评估推理方法的稳健性，以对抗具有意外行为的对手。该平台及其生成的数据集将服务于计算机科学和MAS中更广泛的研究和教育社区。研究稳健的战略推理方法有可能显著改善许多重要现实世界问题的决策，包括国土安全行动的决策支持工具。稳健方法的可用性也将使计算博弈论在尽管存在不确定性但对战略的信心至关重要的领域中得到新的应用。该奖项还支持通过发展UTEP的研究能力来扩大参与，UTEP是一家为少数群体服务的机构。该项目的主要技术贡献是计算博弈论。典型的博弈论解决方案对实际应用中出现的各种不确定性并不健壮，这些不确定性包括支付不确定性、抽象误差和对手建模误差。稳健性需要包含意外情况的技术，以及能够准确表征不确定性的技术。该项目扩展了元游戏的框架，以提供一种在不同类型的不确定性的背景下评估稳健性的方法，包括上面列出的三种不确定性。PI使用元博弈来评估稳健战略推理的新概念，包括基于贝叶斯博弈的方法、基于区间的方法以及从行为博弈论中提取的方法。该项目开发的基于网络的平台为游戏代理的广泛实验提供了便利。这将被用来收集各种各样的意外代理策略(包括本科生设计的策略)，从而能够更全面地调查对意外对手行为的稳健性。

Many important decision problems in computational science involve multi-­agent systems (MAS) in which multiple decision makers must choose strategies for action from a set of alternatives. (One example is selecting a security policy --- for example by police --- to protect critical infrastructure against attackers.) This CAREER award project develops methods for analyzing MAS to select strategies for an agent to follow that will lead to desirable outcomes. The central tenant driving the project is that a good strategy should be robust in the sense that it will continue to perform well even when there is uncertainty about the outcomes or how other agents will choose their strategies. An important component of the project is a multi-purpose web platform supporting both research experiments and education on game-playing agents. The PI will design course modules that use this platform to involve undergraduates in designing game-playing agents, offering an exciting way to practice basic programming and develop critical thinking and data analysis skills. The agents designed by students also play an important role in the research; they will provide a diverse library of strategies to evaluate the robustness of reasoning methods for play against opponents with unanticipated behaviors. This platform, and the data sets generated with it will serve the broader research and education communities in computer science and MAS.Research on robust methods for strategic reasoning has the potential to significantly improve decision making in many important real-world problems, including decision support tools for homeland security operations. The availability of robust methods will also enable new applications of computational game theory in domains where confidence in the strategies, despite uncertainty, is critical. This award also supports broadening participation by developing research capacity at UTEP, a minority-­serving institution. The project's primary technical contributions are in computational game theory. Typical game-theoretic solutions are not robust to the kinds of uncertainty that arise in real applications; these include payoff uncertainty, abstraction error, and opponent modeling error. Robustness requires techniques that encompass unanticipated situations, as well as those where it is possible to precisely characterize the uncertainty. The project extends the framework of meta-games to provide a methodology for evaluating robustness in the context of different kinds of uncertainty, including the three listed above. The PI uses meta-games to evaluate new concepts for robust strategic reasoning, including methods based on Bayesian games, interval-based approaches, and approaches drawn from behavioral game theory. The web-based platform developed in the project facilitates extensive experiments with game-playing agents. This will be used to collect a diverse pool of unanticipated agent strategies (including ones designed by undergraduate students), enabling a more comprehensive investigation of robustness to unanticipated opponent behaviors.