Utile Coordination: Learning Interdependencies Among Cooperative Agents

We describe Utile Coordination, an algorithm that allows a multiagent system to learn where and how to coordinate. The method starts with uncoordinated learners and maintains statistics on expected returns. Coordination dependencies are dynamically added if the statistics indicate a statistically significant benefit. This results in a compact state representation because only necessary coordination is modeled. We apply our method within the framework of coordination graphs in which value rules represent the coordination dependencies between the agents for a specific context. The algorithm is first applied on a small illustrative problem, and next on a large predator-prey problem in which two predators have to capture a single prey.