博弈论与人工智能交叉领域的多智能体学习系统数学
Mathematics of multi-agent learning systems at the interface of game theory and artificial intelligence
AI总结：
该研究梳理演化博弈论与人工智能的关联差异，指出二者思想交叉可支撑多智能体学习系统的数学研究，推动集体合作智能这一新兴领域发展。
摘要：
演化博弈论（Evolutionary Game Theory，EGT）与人工智能（Artificial Intelligence，AI）乍看之下似乎是两个截然不同的领域，但二者存在显著的关联与交叉点。前者聚焦于种群中行为（或策略）的演化过程：种群内的个体与其他个体互动，并基于模仿（或社会学习）更新自身策略；策略越成功，随时间推移其在种群中的普及度就越高。后者则以机器学习算法与（深度）神经网络为核心，通常从单智能体视角展开研究，但如今越来越多地涉及多智能体环境——在这类环境中，智能体基于反馈与经验调整策略，这一过程与演化过程有一定相似性，但二者在自学习能力上存在差异。考虑到解决现实世界问题所需的关键要素，包括（i）学习与适应、（ii）合作与竞争、（iii）鲁棒性与稳定性，以及整体层面的（iv）策略不断演化的个体智能体的种群动力学，两个领域之间的思想交叉融合将推动多智能体学习系统数学研究的发展，尤其会促进连接演化动力学与多智能体强化学习的新兴领域“集体合作智能”的进步。
英文摘要：
Evolutionary Game Theory (EGT) and Artificial Intelligence (AI) are two fields that, at first glance, might seem distinct, but they have notable connections and intersections. The former focuses on the evolution of behaviors (or strategies) in a population, where individuals interact with others and update their strategies based on imitation (or social learning). The more successful a strategy is, the more prevalent it becomes over time. The latter, meanwhile, is centered on machine learning algorithms and (deep) neural networks. It is often from a single-agent perspective but increasingly involves multi-agent environments, in which intelligent agents adjust their strategies based on feedback and experience, somewhat akin to the evolutionary process yet distinct in their self-learning capacities. In light of the key components necessary to address real-world problems, including (i) learning and adaptation, (ii) cooperation and competition, (iii) robustness and stability, and altogether (iv) population dynamics of individual agents whose strategies evolve, the cross-fertilization of ideas between both fields will contribute to the advancement of mathematics of multi-agent learning systems, in particular, to the nascent domain of ``collective cooperative intelligence'' bridging evolutionary dynamics and multi-agent reinforcement learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
