Learning to Form Dynamic Committees
Publication Type:Conference Paper
Source:The second International Conference on Autonomous Agents and Multiagent Systems(AAMAS 2003), ACM Press, p.504-511 (2003)
Learning agents may improve performance when they cooperate with other agents. Specifically, learning agents forming a committee may outperform individual agents. This “ensemble effect” is well known for multi-classifier systems in Machine Learning. However, multi-classifier systems assume all data is known to all classifiers while we focus on agents that learn from cases (examples) that are owned and stored individually. In this article we focus on the selection of the agents that join a committee for solving a problem. Our approach is to frame committee membership as a learning task for the convener agent. The committee convener agent learns to form a committee (and which agent to invite) or to close the membership. The convener agent performs learning in the space of voting situations, i.e. learns when the current committee voting situation is likely to solve correctly (or not) a problem. The learning process allows an agent to decide when to solve a problem individually, when to convene a committee is better, and which individual agents to be invited to join committee. Our experiments show that learning to form dynamic committees results in smaller committees while maintaining (and sometimes improving) the problem solving accuracy.