Training Coordination Proxy Agents using Reinforcement Learning

Contents

In heterogenous multiagent systems where human and non-human agents coexist, intelligent proxy agents can help smooth out fundamental differences. In this context, delegating the coordination role to proxy agents can improve the overall outcome of a task at the expense of human 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. In conclusion, some consequences of the symbiotic relationship that such an approach suggests are discussed.