Agent Selection

Agent selection focuses on optimizing the choice of agents within a multi-agent system to improve performance and efficiency. Current research explores dynamic agent selection methods, often employing techniques like attention mechanisms, graph neural networks, and reinforcement learning, to adapt agent participation based on task demands or environmental conditions. This research is significant because effective agent selection can enhance the performance of complex systems across diverse applications, from trajectory prediction and large language model collaboration to market-making and multi-agent reinforcement learning. Improved agent selection strategies lead to more robust, efficient, and interpretable systems.