Provable Sample Complexity Guarantees for Learning of Continuous-Action Graphical Games with Nonparametric Utilities

In this paper, we study the problem of learning the exact structure of continuous-action games with non-parametric utility functions. We propose an $\ell_1$ regularized method which encourages sparsity of the coefficients of the Fourier transform of the recovered utilities. Our method works by accessing very few Nash equilibria and their noisy utilities. Under certain technical conditions, our method also recovers the exact structure of these utility functions, and thus, the exact structure of the game. Furthermore, our method only needs a logarithmic number of samples in terms of the number of players and runs in polynomial time. We follow the primal-dual witness framework to provide provable theoretical guarantees.Comment: arXiv admin note: text overlap with arXiv:1911.0422

Some Problems on Building Cognitive Robot

人类的智力与人类的涉身体验密不可分。因此，许多人工智能学者相信 在迈向更高级的人工智能的道路上，涉身人工智能是合理甚至是唯一的选择。 涉身人工智能与传统的人工智能的重要区别在于；强调了认知和意识的生 成不仅仅是一个“心智”（Mind）问题。同时，也与其所处环境(Environment)、 身体（Body）密切相关，三者同时在认知的形成过程中扮演着重要的角色； 强调“自我”在认知和意识中的重要作用，即以第一人称视角（First-person Perspective）来体验世界并形成相关的概念和知识，以“自我”为出发点运用这 些知识。 秉持这种思想，结合厦门大学智能科学系正在开...Human intelligence and our embodied experience are inseparable. Therefore, many scholars in AI community believe that the road to human-level intelligence, embodied artificial intelligence (EAI) is a reasonable or even the only option. There are two main differences between EAI and traditional AI methods: at first, EAI highlights that the formation of cognition and consciousness is more than ...学位：博士后院系专业：数学科学学院数学与应用数学系_人工智能基础学号：BHBG0005