Autonomous Multi-Agent Coordination Using Game Theoretic Models in Artificial Intelligence Environments
Keywords:
Multi-Agent Systems, Game Theory, Autonomous Coordination, Artificial Intelligence, Nash Equilibrium, Distributed Decision-MakingAbstract
The field of artificial intelligence (AI) has increasingly incorporated multi-agent systems (MAS) to simulate and solve complex decision-making tasks in distributed environments. As these agents operate autonomously and often with conflicting goals, game theory has emerged as a robust mathematical framework for enabling coordination, negotiation, and equilibrium strategies. This paper explores the integration of game theoretic models within AI-driven multi-agent environments, focusing on cooperative and non-cooperative strategies for coordination, stability, and adaptability in uncertain and dynamic domains. Through simulation-based evaluation and comparative analysis, we demonstrate how game theory supports scalable and conflict-resilient decision-making in autonomous multi-agent ecosystems.
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