Machine Learning
Emergence of stable coalitions via task exchanges
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Cooperative CBR System for Peer Agent Committee Formation
Agents and Peer-to-Peer Computing
Collaborative case retention strategies for CBR agents
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Self-organising hierarchical retrieval in a case-agent system
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
COBRAS: cooperative CBR system for bibliographical reference recommendation
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
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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 in a dynamic way: at each point in time the convener agent decides whether it is better to invite a new member to join the 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 the committee. Our experiments show that learning to form dynamic committees results in smaller committees while maintaining (and sometimes improving) the problem solving accuracy.