智能博弈对手建模方法研究综述OA

Opponent modeling is a key technique in intelligent gaming that improves decision-making in dynamic,uncer-tain environments by inferring and predicting adversary behaviors,strategies,and intentions.It has broad applications in domains such as game AI,military simulation,and cybersecurity.This paper systematically reviews fundamental concepts,major challenges,and methodological frameworks for opponent modeling.A classification scheme based on learning mechanisms organizes existing methods into three categories:parameter modeling,strategy modeling,and relation modeling.Each category is analyzed in terms of its core principles,representative algorithms,applicable contexts,and limitations.The paper also examines the trend toward hybrid modeling and identifies future research directions,including bounded rationality modeling,the integration of large language models,and considerations of interpretability and safety.This work provides a reference for ongoing research in the field.