第四十六期

报告人:Brian Hu Zhang,  Carnegie Mellon University

时间:8月9日(星期五)10:00am

方式:Zoom 在线会议

          会议号: 824 5030 5971           密码: 563216

Host:陈昱蓉博士,前沿计算研究中心2019级

报告信息

Title

Steering No-Regret Learners and Hidden-Role Games

Abstract

This talk will cover two papers both of which I presented at EC 2024. The two halves of the talk will be essentially independent of each other.
The first half of the talk will be about steering no-regret learners to a desired equilibrium. A mediator observes no-regret learners playing an extensive-form game repeatedly across T rounds. The mediator attempts to steer players toward some desirable predetermined equilibrium by giving (nonnegative) payments to players. We call this the steering problem. The steering problem captures several problems of interest, among them equilibrium selection and information design (persuasion). If the mediator's budget is unbounded, steering is trivial because the mediator can simply pay the players to play desirable actions. We study two bounds on the mediator's payments: a total budget and a per-round budget. If the mediator's total budget does not grow with T, we show that steering is impossible. However, we show that it is enough for the total budget to grow sublinearly with T, that is, for the average payment to vanish. When players' full strategies are observed at each round, we show that constant per-round budgets permit steering. In the more challenging setting where only trajectories through the game tree are observable, we show that steering is impossible with constant per-round budgets in general extensive-form games, but possible in normal-form games or if the per-round budget may itself depend on T. We also show how our results can be generalized to the case when the equilibrium is being computed online while steering is happening. We supplement our theoretical positive results with experiments highlighting the efficacy of steering in large games.
The second half of the talk will be about the class of games known as hidden-role games, in which players are assigned privately to teams and are faced with the challenge of recognizing and cooperating with teammates. This model includes both popular recreational games such as the Mafia/Werewolf family and The Resistance (Avalon) and many real-world settings, such as distributed systems where nodes need to work together to accomplish a goal in the face of possible corruptions. There has been little to no formal mathematical grounding of such settings in the literature, and it was previously not even clear what the right solution concepts (notions of equilibria) should be. A suitable notion of equilibrium should take into account the communication channels available to the players (e.g., can they communicate? Can they communicate in private?). Defining such suitable notions turns out to be a nontrivial task with several surprising consequences. In this paper, we provide the first rigorous definition of equilibrium for hidden-role games, which overcomes serious limitations of other solution concepts not designed for hidden-role games. We then show that in certain cases, including the above recreational games, optimal equilibria can be computed efficiently. In most other cases, we show that computing an optimal equilibrium is at least NP-hard or coNP-hard. Lastly, we experimentally validate our approach by computing exact equilibria for complete 5- and 6-player Avalon instances whose size in terms of number of information sets is larger than 10^56.

Biography

I am a final-year PhD student in the Computer Science Department at Carnegie Mellon University, where I am fortunate to be advised by Prof. Tuomas Sandholm. I am supported by the CMU Hans J. Berliner Graduate Fellowship in Artificial Intelligence. My current research interests lie in computational game theory, especially equilibrium computation in extensive-form games; subgame solving; no-regret learning in games; automated mechanism design; adversarial team games; and solution concepts involving correlation, communication, and/or mediation. I have also done work in adversarial robustness, fairness in machine learning, and quantum computing. Prior to CMU, I completed my undergraduate and master’s degrees at Stanford University, where I worked with Prof. Greg Valiant.

about CS Peer Talk

作为活动的发起人,我们来自北京大学图灵班科研活动委员会,主要由图灵班各年级同学组成。我们希望搭建一个CS同学交流的平台,促进同学间的交流合作,帮助同学练习展示,同时增进友谊。

目前在计划中的系列包括但不限于:

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    研究系列学生讲者为主,介绍自己的研究成果

    客座系列邀请老师做主题报告

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