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This paper explores how heterogeneous AI agents with varying capabilities and costs can delegate tasks among themselves to complete research tasks efficiently under resource constraints. It focuses on collaborative multi-agent systems using reinforcement learning to optimize decision-making.
This paper explores how heterogeneous AI agents with varying capabilities and costs can delegate tasks among themselves to complete research tasks efficiently under resource constraints. It focuses on collaborative multi-agent systems using reinforcement learning to optimize decision-making.
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AI agents are expected to play an increasingly important role in future decision-making systems. In this paper, we consider collaborative systems composed of heterogeneous multi-agent systems (MAS), where their members have different capabilities and operating costs. We study how agents can delegate tasks to one...
Understanding task delegation in multi-agent systems can improve the efficiency and effectiveness of AI-driven decision-making in complex, resource-limited environments. This research advances methods for coordinating diverse AI agents to achieve shared goals.
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