Classical game-theoretic models typically assume rational agents, complete information, and common knowledge of payoffs - assumptions that are often violated in real-world MAS characterized by uncertainty, misaligned perceptions, and nested beliefs. To overcome these limitations, researchers have proposed extensions that incorporate models of cognitive constraints, subjective beliefs, and heterogeneous reasoning. Among these, hypergame theory extends the classical paradigm by explicitly modeling agents' subjective perceptions of the strategic scenario, known as perceptual games, in which agents may hold divergent beliefs about the structure, payoffs, or available actions. We present a systematic review of agent-compatible applications of hypergame theory, examining how its descriptive capabilities have been adapted to dynamic and interactive MAS contexts. We analyze 44 selected studies from cybersecurity, robotics, social simulation, communications, and general game-theoretic modeling. Building on a formal introduction to hypergame theory and its two major extensions - hierarchical hypergames and HNF - we develop agent-compatibility criteria and an agent-based classification framework to assess integration patterns and practical applicability. Our analysis reveals prevailing tendencies, including the prevalence of hierarchical and graph-based models in deceptive reasoning and the simplification of extensive theoretical frameworks in practical applications. We identify structural gaps, including the limited adoption of HNF-based models, the lack of formal hypergame languages, and unexplored opportunities for modeling human-agent and agent-agent misalignment. By synthesizing trends, challenges, and open research directions, this review provides a new roadmap for applying hypergame theory to enhance the realism and effectiveness of strategic modeling in dynamic multi-agent environments.

翻译：经典博弈论模型通常假设理性智能体、完全信息及收益的公共知识——这些假设在现实世界具有不确定性、感知错位和嵌套信念特征的多智能体系统中常被违背。为克服这些局限，研究者提出了融合认知约束模型、主观信念与异质推理的扩展理论。其中，超博弈理论通过显式建模智能体对战略场景的主观感知（即感知博弈）扩展了经典范式，在感知博弈中智能体可能对博弈结构、收益或可行行动持有分歧信念。本文系统综述了超博弈理论的智能体兼容应用，考察其描述能力如何适配动态交互式多智能体场景。我们分析了来自网络安全、机器人学、社会模拟、通信及通用博弈论建模领域的44项研究。在形式化介绍超博弈理论及其两大扩展——分层超博弈与HNF（超博弈范式）的基础上，我们建立了智能体兼容性准则与基于智能体的分类框架，以评估集成模式与实际适用性。分析揭示了当前主流趋势，包括欺骗性推理中分层与图模型的普遍应用，以及实践应用中对扩展理论框架的简化处理。我们识别出结构性空白，包括HNF模型采纳有限、形式化超博弈语言缺失，以及人机对齐与机机对齐建模的未探索机遇。通过综合发展趋势、挑战与开放研究方向，本综述为应用超博弈理论提升动态多智能体环境中战略建模的真实性与有效性提供了新的路线图。