Recognition and interfere deceptive behavior based on inverse reinforcement learning and game theory

作     者：ZENG Yunxiu XU Kai ZENG Yunxiu;XU Kai

作者机构：College of Systems EngineeringNational University of Defense TechnologyChangsha 410073China 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2023年第34卷第2期

页      面：270-288页

核心收录：

学科分类：1305[艺术学-设计学（可授艺术学、工学学位）] 12[管理学] 13[艺术学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 070105[理学-运筹学与控制论] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：deceptive path planning inverse reinforcement learning(IRL) game theory goal recognition 

摘      要：In real-time strategy(RTS)games,the ability of recognizing other players’goals is important for creating artifical intelligence(AI)***,most current goal recognition methods do not take the player’s deceptive behavior into account which often occurs in RTS game scenarios,resulting in poor recognition *** order to solve this problem,this paper proposes goal recognition for deceptive agent,which is an extended goal recognition method applying the deductive reason method(from general to special)to model the deceptive agent’s behavioral *** of all,the general deceptive behavior model is proposed to abstract features of deception,and then these features are applied to construct a behavior strategy that best matches the deceiver’s historical behavior data by the inverse reinforcement learning(IRL)***,to interfere with the deceptive behavior implementation,we construct a game model to describe the confrontation scenario and the most effective interference measures.