Adaptive strategy automation with large language models in paradoxical games - Kang Hao Cheong; Jie Zhao; Tao Wen (Physical Review Research)
Finding an ideal sequence in games is crucial for maximizing gains in various scenarios. This process requires extensive investigation, which is both time consuming and demands significant domain expertise. The emergence of large language models (LLMs) represents a significant turning point in addressing this issue, attributable to their potent analytical capabilities. In this context, LLMs can serve to substantially alleviate the human labor needed to manage these complexities. Through comprehensive simulations of coin-tossing games, we have demonstrated that the adaptive switching strategies formulated by LLMs surpass predefined sequences in profitability when applied to certain paradoxical games. Furthermore, our experimental findings indicate that the proposed method not only automates the identification of effective strategies but also provides adaptability to various forms of these paradoxical games.