A Proposal for Meta-Learning Through a Multi-Agent System

Authors:
Juan A. Botía;Antonio F. Gómez-Skarmeta;Juan R. Velasco;Mercedes Garijo
Affiliations:
-;-;-;-
Venue:
Revised Papers from the International Workshop on Infrastructure for Multi-Agent Systems: Infrastructure for Agents, Multi-Agent Systems, and Scalable Multi-Agent Systems
Year:
2000

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Abstract

The meta-learning problem has become an important issue in the recent years. This has been caused by the growing role of datamining applications in the global information systems of big companies which want to obtain benefits from the analysis of its data. It is necessary to obtain faithfull application rules that guide the datamining process in order to achieve the best possible models that explain the databases. We follow an inductive approach to discover these kind of rules. This paper explains the MAS-based information system we use for mining and meta-learning, and how the scalability problem is solved in order to support a community of many software agents.