Multi-agent ad hoc team partitioning by observing and modeling single-agent performance

Multi-agent research has focused on finding the optimal team for a task. Many approaches assume that the performance of the agents are known a priori. We are interested in ad hoc teams, where the agents´ algorithms and performance are initially unknown. We focus on the task of modeling the performance of single agents through observation in training environments, and using the learned models to partition a new environment for a multi-agent team. The goal is to minimize the number of agents used, while maintaining a performance threshold of the multi-agent team. We contribute a novel model to learn the agent´s performance through observations, and a partitioning algorithm that minimizes the team size. We evaluate our algorithms in simulation, and show the efficacy of our learn model and partitioning algorithm.