文章基本信息

标题：Optimally Deceiving a Learning Leader in Stackelberg Games
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作者：Georgios Birmpas ; Jiarui Gan ; Alexandros Hollender 等
期刊名称：Journal of Artificial Intelligence Research
印刷版ISSN：1076-9757
出版年度：2021
卷号：72
页码：1-25
DOI：10.1613/jair.1.12542
语种：English
出版社：American Association of Artificial
摘要：Recent results have shown that algorithms for learning the optimal commitment in a Stackelberg game are susceptible to manipulation by the follower. These learning algorithms operate by querying the best responses of the follower who consequently can deceive the algorithm by using fake best responses typically by responding according to fake payoffs that are different from the actual ones. For this strategic behavior to be successful the main challenge faced by the follower is to pinpoint the fake payoffs that would make the learning algorithm output a commitment that benefits them the most. While this problem has been considered before the related literature has only focused on a simple setting where the follower can only choose from a finite set of payoff matrices thus leaving the general version of the problem unanswered. In this paper we fill this gap by showing that it is always possible for the follower to efficiently compute (near-)optimal fake payoffs for various scenarios of learning interaction between the leader and the follower. Our results also establish an interesting connection between the follower’s deception and the leader’s maximin utility: through deception the follower can induce almost any (fake) Stackelberg equilibrium if and only if the leader obtains at least their maximin utility in this equilibrium.
关键词：game theory;autonomous agents