Mathematics of multi-agent learning systems at the interface of game theory and artificial intelligence
arxiv(2024)
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.
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