Cooperative learning in a multi-agent system can improve the learning quality and learning speed. The improvement can be gained if each agent detects the expert agents and uses their knowledge properly. In the paper, a cooperative learning method, called weighted strategy sharing (WSS) is introduced. Also some criteria are introduced to measure the expertness of agents. In WSS, based on the amount of its team-mate expertness, each agent assigns a weight to their knowledge. These weights are used in sharing knowledge among agents in our system. WSS and the expertness criteria are tested on two simulated hunter-prey problems and on object pushing systems.
['M.N. Ahmadabadi', 'M. Asadpur', 'S.H. Khodanbakhsh', 'E. Nakano']
Expertness measuring in cooperative learning