Abstract
Growing importance of distributed data mining techniques has recently attracted attention of researchers in multiagent domain. In this paper we present a novel framework MultiAgent Learning Framework (MALEF) designed for both the agent-based distributed machine learning as well as data mining. Proposed framework is based on: the exchange of meta-level descriptions of individual learning process; online reasoning about learning success and learning progress. This paper illustrates how MALEF framework can be used in practical system in which different learners use different datasets, hypotheses and learning algorithms. We describe our experimental results obtained using this system and review related work on the subject.
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