Optimizing Multi-Agent Coordination in AI-Based Strategic Environments Using Game Theory
Keywords:
Multi-Agent Systems, Game Theory, Strategic Coordination, Nash Equilibrium, AI OptimizationAbstract
In multi-agent systems (MAS), optimal coordination among agents is critical in strategic environments, especially those involving artificial intelligence (AI). Game theory provides a structured and mathematically grounded framework to model, analyze, and optimize these interactions. This paper explores the use of cooperative and non-cooperative game-theoretic models to enhance decision-making, coordination, and conflict resolution in AI-based strategic systems. We examine key literature, propose a hybrid coordination model using both Nash Equilibrium and coalition game theory, and provide simulation results to demonstrate performance. The findings suggest that integrating game-theoretic approaches into MAS leads to improved robustness, reduced conflict, and faster convergence to optimal strategies
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