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

Abstract

In real-time strategy (RTS) games, the ability of recognizing other players' goals is important for creating artifical intelligence (AI) players. However, 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 results. In 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 strategy. First 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) method. Final, to interfere with the deceptive behavior implementation, we construct a game model to describe the confrontation scenario and the most effective interference measures.

Recent Developments of Game Theory and Reinforcement Learning Approaches: A Systematic Review

“…Enhanced performance compared to traditional algorithms By abstracting deception features, the general deceptive behavior model is used to design a behavior plan that best matches the deceiver's past behavior data using inverse reinforcement learning (IRL) [54]. Yifei and Lakshminarayanan plan to configure RL agents for MARL control systems to show their efficacy in managing multiloop processes with significant interconnections.…”