Training Coordination Proxy Agents

Delegating the coordination role to proxy agents can improve the overall outcome of the task at the expense of cognitive overload due to switching subtasks. Stability and commitment are characteristics of human teamwork but must not prevent the detection of better opportunities. In addition, coordination proxy agents must be trained from examples as a single agent but must interact with multiple agents. We apply machine learning techniques to the task of learning team preferences from mixed-initiative interactions and compare the outcome results of different simulated user patterns. This paper introduces a novel approach for the adjustable autonomy of coordination proxies based on the reinforcement learning of abstract actions.