Leveraging Noisy Observations in Zero-Sum Games

Autor:	Athanasakos, Emmanouil M, Perlaza, Samir M
Rok vydání:	2024
Předmět:	Computer Science - Computer Science and Game Theory Computer Science - Information Theory Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	This paper studies an instance of zero-sum games in which one player (the leader) commits to its opponent (the follower) to choose its actions by sampling a given probability measure (strategy). The actions of the leader are observed by the follower as the output of an arbitrary channel. In response to that, the follower chooses its action based on its current information, that is, the leader's commitment and the corresponding noisy observation of its action. Within this context, the equilibrium of the game with noisy action observability is shown to always exist and the necessary conditions for its uniqueness are identified. Interestingly, the noisy observations have important impact on the cardinality of the follower's set of best responses. Under particular conditions, such a set of best responses is proved to be a singleton almost surely. The proposed model captures any channel noise with a density with respect to the Lebesgue measure. As an example, the case in which the channel is described by a Gaussian probability measure is investigated. Comment: This paper is submitted to the 2024 IEEE International Symposium on Information Theory (ISIT 2024)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.02861 Zobrazit plný text záznamu View this record from Arxiv