Expertness measuring in cooperative learning

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...

Full description

Saved in:
Bibliographic Details
Published inProceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113) Vol. 3; pp. 2261 - 2267 vol.3
Main Authors Ahmadabadi, M.N., Asadpur, M., Khodanbakhsh, S.H., Nakano, E.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2000
Subjects
Humans
Immune system
Intelligent robots
Intelligent systems
Laboratories
Learning systems
Mathematics
Multiagent systems
Physics
System testing
Online AccessGet full text

Cover

More Information
Summary: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.
ISBN:9780780363489
0780363485
DOI:10.1109/IROS.2000.895305