Managing multiple agents by automatically adjusting incentives
This addresses coordination challenges in multi-agent systems for applications like supply-chain management, but it is incremental as it builds on existing incentive-based methods.
The paper tackles the problem of self-interested AI agents not aligning with societal goals by proposing a manager agent that adjusts incentives, resulting in a 22.2% increase in raw reward, 23.8% increase in agents' reward, and 20.1% increase in manager's reward in a supply-chain management test.
In the coming years, AI agents will be used for making more complex decisions, including in situations involving many different groups of people. One big challenge is that AI agent tends to act in its own interest, unlike humans who often think about what will be the best for everyone in the long run. In this paper, we explore a method to get self-interested agents to work towards goals that benefit society as a whole. We propose a method to add a manager agent to mediate agent interactions by assigning incentives to certain actions. We tested our method with a supply-chain management problem and showed that this framework (1) increases the raw reward by 22.2%, (2) increases the agents' reward by 23.8%, and (3) increases the manager's reward by 20.1%.