In the empirical approach to game-theoretic analysis (EGTA), the model of the game comes not from declarative representation, but is derived by interrogation of a procedural description of the game environment. The motivation for developing this approach was to enable game-theoretic reasoning about strategic situations too complex for analytic specification and solution. Since its introduction over twenty years ago, EGTA has been applied to a wide range of multiagent domains, from auctions and markets to recreational games to cyber-security. We survey the extensive methodology developed for EGTA over the years, organized by the elemental subproblems comprising the EGTA process. We describe key EGTA concepts and techniques, and the questions at the frontier of EGTA research. Recent advances in machine learning have accelerated progress in EGTA, and promise to significantly expand our capacities for reasoning about complex game situations.

翻译：在博弈论分析的经验性方法（EGTA）中，博弈模型并非源于声明式表示，而是通过对博弈环境程序化描述的查询推导而来。发展这一方法的动机，是为过于复杂而无法进行解析性描述和求解的战略情境提供博弈论推理能力。自二十多年前提出以来，EGTA已被广泛应用于从拍卖和市场到休闲游戏再到网络安全的多个多智能体领域。我们综述了多年来为EGTA开发的大量方法论，并按构成EGTA过程的基本子问题进行组织。我们描述了关键的EGTA概念与技术，以及EGTA研究前沿的问题。机器学习的最新进展加速了EGTA的发展，并有望显著扩展我们对复杂博弈情境的推理能力。