How can agents learn in a changing environment?
Decentralized Hidden Markov Modeling with Equal Exit Probabilities
This paper explores how networked agents can learn the true state of a changing environment by combining their own observations with the beliefs of their neighbors. It focuses on a simplified model where the environment's state changes with equal probability to any other state. A "Diffusion α-HMM" strategy is proposed and analyzed, showing how agents' beliefs evolve over time.
For LLM-based multi-agent systems, the key takeaway is the analysis of belief updates in dynamic environments. The simplified model and the concept of "exit probability" (α) offer a controllable parameter for tuning how much an agent trusts its own observations versus its neighbors' beliefs, which is relevant to belief fusion in multi-agent LLM systems. The paper's analysis of convergence and steady-state error provides insights into the long-term behavior of such systems, particularly the trade-off between adaptability and accuracy in noisy, changing environments. This has implications for designing robust and efficient communication strategies in LLM-based multi-agent applications.