Leveraging Organizational Guidance Policies with Learning to Self-Tune Multiagent Systems

As organization-based multiagent systems are applied to more complex problems, configuring and tuning the systems can become nearly as complex as the original problem a system was designed to solve. A robust system should be able to adapt. It should be able to self-configure and self-tune. To this end, we propose a method for self-tuning using the concept of guidance policies, that is policies that are designed to guide the system without sacrificing its flexibility. Guidance policies allow us to apply traditional learning techniques online without many of the drawbacks associated with a system falling into a local optimum. They also help simplify the learning process. We examine the impact of this learning on various multiagent systems.