TítuloJustification-based multiagent learning
Publication TypeConference Paper
Year of Publication2003
AuthorsOntañón S, Plaza E
EditorFawcett T, Mishra N
Conference NameThe Twentieth International Conference on Machine Learning (ICML 2003)
EditorialAAAI Press

Committees of classifiers whit learning capabilities have good performance in a variety of domains. We focus on committees of agents whit learning capabilities where no agents omniscient but has a local, limited, individual view of data. In this framework, a major issue is how to integrate the individual results in an overall result-usually a voting mechanism is used. We propose a setting where agents can express a symbolic justifications can their individual results. Justifications can then be examined by other agents and accepted or found wanting. We propose a specific interaction protocol that supports revision of justifications created by different agents. Finally, the opinions of individual agents are aggregated into a global outcome using a weighted voting scheme.