We propose a game-theoretic framework for adaptive multi-agent intelligent systems. Unlike classical game theory, which often treats strategies as primitive objects chosen by perfectly rational agents, the proposed framework provides a mathematical foundation for studying equilibrium in NeuroAI and can be viewed as an extension of game theory under relaxed assumptions, including partial observability, bounded computation, and uncertainty. At its core, Multilevel Interactive Equilibrium (MIE) generalizes the classical Nash equilibrium to intelligent systems with internal computation. Rather than being defined solely at the level of observable behavior, equilibrium emerges when neural learning dynamics, cognitive representations, and behavioral strategies mutually stabilize between interacting agents. This framework applies uniformly to interactions between two biological brains, two artificial agents, or hybrid human-AI systems. We discuss applications of multilevel game theory to human-autonomous vehicle driving, human-machine interaction, human-large language model (LLM) interaction, and computational psychiatry. We also outline experimental strategies and computational methods for estimating MIE and discuss challenges and prospects for future research.

翻译：我们提出一个针对自适应多智能体系统的博弈论框架。与经典博弈论将策略视为完全理性智能体选择的原始对象不同，本框架为研究神经人工智能中的均衡提供了数学基础，可视为在放宽假设条件下博弈论的扩展，包括部分可观测性、有限计算能力和不确定性。其核心概念——多级互动均衡（MIE）将经典纳什均衡推广至具有内部计算能力的智能系统。该均衡并非仅定义在可观察行为层面，而是在神经网络学习动态、认知表征与行为策略在互动智能体间相互稳定时涌现。该框架统一适用于两个生物大脑、两个人工智能体或人机混合系统之间的互动。我们讨论了多级博弈论在人机共驾、人机交互、人类与大型语言模型（LLM）互动以及计算精神病学中的应用。同时概述了估算多级互动均衡的实验策略与计算方法，并探讨了未来研究的挑战与前景。