In this paper, we consider a distributed Bayesian Nash equilibrium (BNE) seeking problem in incomplete-information aggregative games, which is a generalization of Bayesian games and deterministic aggregative games. We handle the aggregation function for distributed incomplete-information situations. Since the feasible strategies are infinite-dimensional functions and lie in a non-compact set, the continuity of types brings barriers to seeking equilibria. To this end, we discretize the continuous types and then prove that the equilibrium of the derived discretized model is an $\epsilon$-BNE. On this basis, we propose a distributed algorithm for an $\epsilon$-BNE and further prove its convergence.

翻译：在本文中,我们考虑到一个分布式的巴伊西亚纳什均衡(BNE)在信息不全的分类游戏中寻找问题,即泛泛的巴伊西亚游戏和决定性的分类游戏。我们处理分布式不完全信息情况的汇总功能。由于可行的战略是无限的功能,并存在于非契约集中,因此各种类型的连续性为寻求平衡带来了障碍。为此,我们分解了连续的种类,然后证明衍生的离散模型的平衡是$-epsilon$-BNE。在此基础上,我们提议一个分布式算法,用于一个$-epsilon-BNE,并进一步证明其趋同。