Game Theory in Artificial Intelligence and Machine Learning: An Integrated Computational Framework Based on Bibliometrics, Topic Modeling, and HJ-Biplot

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Game Theory provides a foundation for multi-agent systems, reinforcement learning, mechanism design, and adversarial learning within AI and ML. Nevertheless, few comprehensive studies map the structure and branches of this cross-disciplinary field. The current research attempts to fill this gap by providing a combined bibliometric and semantic study of 6974 records obtained from Scopus and Web of Science during the years 1972 to 2025. This research is among the first to implement traditional bibliometrics integrated with LDA (Latent Dirichlet Allocation) topic modeling through R (version 4.5.1) to map research networks and surface latent research themes across domains such as multi-agent reinforcement learning and adversarial learning. The major contribution is the development of an evolutionary model that identifies the Game Theory’s paradigmatic evolution in the context of AI and ML from 1972 to 2025. This research also provided the characteristics that describe the shift from the rational equilibrium paradigms to intelligent systems that learn, collaborate, and devise strategies autonomously.