Knowledge Resource Allocation Game (KRAG): A Novel Algorithm for Fair and Adaptive Resource Distribution in Multi-Agent Systems

This paper introduces KRAG (Knowledge Resource Allocation Game), a novel algorithmic framework designed to address the challenges of resource allocation in multi-agent systems. In dynamic environments, agents with competing interests and varying levels of contribution often struggle to find a balance between fairness and efficiency. Traditional allocation methods fail to adapt to such complexities, leading to imbalances in resource distribution. KRAG leverages principles from cooperative game theory, specifically the Shapley value, to ensure a fair distribution of resources based on each agent's contribution. Additionally, the algorithm incorporates reinforcement learning to enable adaptive bidding strategies, allowing agents to continuously learn and adjust their behavior over time. By combining these techniques, KRAG provides a robust solution that not only ensures fairness but also adapts to the evolving needs and strategies of agents, promoting optimal resource management in competitive and dynamic environments. This paper explores the theoretical foundations of KRAG, its implementation, and its application to real-world resource allocation problems, with a focus on fairness, efficiency, and adaptability.