From Historical Strategies to Computational Paradigms: A Study on the AI Algorithmic Inspirations from the Core Wisdom of Zhan Guo Ce

At a time when Artificial Intelligence (AI) urgently needs to transcend traditional research paths, this paper delves into the wisdom of Zhan Guo Ce (Strategies of the Warring States), such as “assessing situations” and “vertical and horizontal alliances.” Employing mathematical models including game theory, optimization algorithms, network science, decision trees, and Bayesian inference, this research uncovers the value of ancient strategies for modern AI. By deconstructing power confrontations through game theory, we reveal strategic equilibrium mechanisms, providing a basis for multi-agent decision-making. Optimization algorithms are utilized to simulate the strategists’ process of weighing pros and cons, aiding AI in resource allocation and path planning. Network science is leveraged to analyze power relationships, optimizing AI network architectures. Decision trees are used to delineate strategists’ decision logic, thereby improving AI decision tree algorithms. Bayesian inference is applied to handle uncertain information, enhancing AI’s environmental adaptability. This study ultimately reveals the mathematical essence of strategic thinking, decision logic, and alliance structures in historical strategies, offering an interdisciplinary perspective for AI algorithm design, decision optimization, and ethical construction, thereby propelling AI towards more intelligent, rational, and reliable development.