To address the limitations of these approaches, we propose CHARMS, a decision-making model based on Level-k game theory [20]. The distinction between our approach and the existing methods is illustrated in Figure 1. CHARMS incorporates cognitive hierarchy theory to model diverse reasoning depths among agents, coupled with Social V alue Orientation (SVO) to capture individual preferences in driving behavior. We employ a two-stage training process consisting of reinforcement learning pretraining and supervised fine-tuning (SFT) to generate decision-making models that exhibit a wide range of human-like driving styles. Additionally, we integrate Poisson cognitive hierarchy (PCH) theory to enable CHARMS to generate more complex simulation scenarios with diverse vehicle styles. The main contributions of this paper can be summarized as follows. A behavior model integrating Level-k reasoning and SVO is proposed to simulate cognitively diverse driving styles. A two-stage training scheme (DRL + SFT) ensures both style distinctiveness and behavioral realism. A scenario generation method based on PCH theory is used to control driving style distributions, with the aim of creating more realistic and behaviorally diverse simulation scenarios.

Beating humans in a penny-matching game by leveraging cognitive hierarchy theory and Bayesian learning Ran Tian, Nan Li, Ilya Kolmanovsky, and Anouck Girard Abstract -- It is a longstanding goal of artificial intelligence (AI) to be superior to human beings in decision making. Games are suitable for testing AI capabilities of making good decisions in non-numerical tasks. In this paper, we develop a new AI algorithm to play the penny-matching game considered in Shannon's "mind-reading machine" (1953) against human players. In particular, we exploit cognitive hierarchy theory and Bayesian learning techniques to continually evolve a model for predicting human player decisions, and let the AI player make decisions according to the model predictions to pursue the best chance of winning. Experimental results show that our AI algorithm beats 27 out of 30 volunteer human players. I NTRODUCTION Developing artificial intelligence (AI) to beat humans in strategic games has been drawing attention/interest of researchers for decades [1]-[10].

Abstract-- In this paper, we describe a framework for autonomous decision making in a dynamic and interactive environment based on cognitive hierarchy theory. We model the in - teractions between the ego agent and its operating environm ent as a two-player dynamic game, and integrate cognitive behav - ioral models, Bayesian inference, and receding-horizon op timal control to define a dynamically-evolving decision strategy for the ego agent. Simulation examples representing autonomou s vehicle control in three traffic scenarios where the autonom ous ego vehicle interacts with a human-driven vehicle are repor ted. Autonomous systems are becoming more capable, better accepted, and more commonplace. Many autonomous systems, including collaborative robots [1] and self-driv ing cars [2], operate in dynamic and interactive environments.