|Título||Justification-based multiagent learning|
|Publication Type||Conference Paper|
|Year of Publication||2003|
|Authors||Ontañón S, Plaza E|
|Editor||Fawcett T, Mishra N|
|Conference Name||The Twentieth International Conference on Machine Learning (ICML 2003)|
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.
- Acerca del IIIA