Game Theory and Its Implications in Decision-Making for Autonomous Systems

Abstract

Game theory provides a powerful framework for analyzing strategic interactions among rational agents, making it highly relevant to decision-making in autonomous systems. As autonomous technologies, including self-driving vehicles, drones, and robotic systems, become increasingly prevalent, understanding their interactions within complex environments is crucial. This paper explores the application of game theory to these systems, focusing on how it can optimize decision-making processes in various scenarios. We delve into cooperative and non-cooperative game models, examining their implications for traffic management, multi-agent coordination, and security. By applying game-theoretic approaches, we can enhance the efficiency and safety of autonomous systems, addressing challenges such as high-dimensionality, dynamic environments, and ethical considerations. The paper also highlights the need for advanced models that integrate machine learning and real-time adaptation to better manage the evolving nature of autonomous systems. Future research directions include interdisciplinary approaches and the establishment of standards for ethical use. This comprehensive analysis underscores the significance of game theory in developing robust, adaptive, and fair decision-making frameworks for autonomous systems, ultimately contributing to their successful deployment and operation.