How to control bias in multi-agent systems for task allocation?
Adaptive bias for dissensus in nonlinear opinion dynamics with application to evolutionary division of labor games
This paper presents a method for decentralized control of a group of agents to maximize a collective reward, using a combination of nonlinear opinion dynamics and an adaptive bias. The bias incentivizes agents to divide into subgroups according to an evolving estimate of the optimal distribution for maximizing rewards, even when the reward structure is initially unknown.
The relevance to LLM-based multi-agent systems is that this method provides a way to coordinate large numbers of agents with limited communication, enabling them to adapt their behavior and learn optimal strategies for achieving shared goals. This could be applied to scenarios like decentralized resource allocation, task distribution, or collaborative problem solving.