Justification-based Multiagent Learning

Proceedings of the Twentieth International Conference on Machine Learning

Volume

Proceedings of the Twentieth International Conference on Machine Learning

Contents

Committees of classiffiers with learning capabilities have good performance in a variety of domains. We focus on committees of agents with learning capabilities where no agent is 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 justification of 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.