Sharing in Teams of Heterogeneous, Collaborative Learning Agents

This paper is focused on the effects of sharing knowledge and collaboration of multiple heterogenous, intelligent agents (hardware or software) which work together to learn a task. As each agent employ's a different machine learning technique, the system consists of multiple knowledge sources and their respective heterogeneous knowledge representations. Collaboration between agents involves sharing knowledge to both speed up team learning, as well as refine the team's overall performance and group behavior. Experiments have been performed that vary the learn composition in terms of machine learning algorithms. learning strategies employed by the agents, and sharing frequency for a predator-prey cooperative pursuit task. For lifelong learning heterogenous learning teams were more successful than homogenous learning counterparts. Interestingly, sharing increased the learning rate, but sharing with higher frequency showed diminishing results. Lastly. knowledge conflicts are reduced over time the more sharing takes place. These results support further investigation of the merits of heterogenous learning. (C) 2008 Wiley Periodicals. Inc.