Recycling Data for Multi-Agent Learning

Abstract

Learning agents can improve performance cooperating with other agents, particularly learning agents forming a committee 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 how individual agents can engage in bargaining activities that improve the performance of both individual agents and the committee. The agents are capable of self-evaluation and determining that some data used for learning is unnecessary. This "refuse" data can then be exploited by other agents that might found some part of it profitable to improve their performance. The experiments we performed show that this approach improves both individual and committee performance and we analyze how these results in terms of the "ensemble effect".