Enhancing Multi-Agent Decision-Making: Integrating Artificial Neural Networks with Game Theory in the Neural Game Equilibrium (NGE) Algorithm

Abstract

In complex and dynamic environments, the process of decision-making among multiple agents faces significant challenges, especially when it comes to swift adaptation, strategic planning, and maximizing results for all agents. This paper presents the Neural Game Equilibrium (NGE) algorithm, an novel approach that integrates the predictive capabilities of Artificial Neural Networks (ANNs) with the intricate strategies of game-theoretic concepts. The NGE algorithm is designed to transform multi-agent interaction, allowing agents to proactively predict and adapt to the strategies of their counterparts, with the goal of achieving a seamless equilibrium that enhances both personal and group objectives.The NGE algorithm utilizes sophisticated recurrent neural networks to provide real-time predictive insights, with Nash equilibrium-based strategy modification, enabling agents to achieve exceptional flexibility, stability, and alignment in uncertain situations. NGE exhibits a substantial enhancement over conventional methods via simulations in expected applications, including autonomous systems and resource distribution frameworks, establishing new benchmarks to evaluate efficiency, collaboration, and strategic coherence among agents. Our research indicates that the NGE algorithm is poised to revolutionize multi-agent intelligence, providing significant improvements in decision quality, robustness, and scalability in domains where immediate responses and coordinated strategy are essential. NGE advances the limits of intelligent synchronization and adaptive decision-making, serving as an innovative instrument for the next phase of multi-agent systems and representing a notable progression in the development of autonomous, strategically complex agent interactions.