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Classification of some objects in classes of concepts is an essential andeven breathtaking task in many applications. A solution is discussed here basedon Multi-Agent systems. A kernel of some expert agents in several classes is toconsult a central agent decide among the classification problem of a certainobject. This kernel is moderated with the center agent, trying to manage thequerying agents for any decision problem by means of a data-header like featureset. Agents have cooperation among concepts related to the classes of thisclassification decision-making; and may affect on each others' results on acertain query object in a multi-agent learning approach. This leads to anonline feature learning via the consulting trend. The performance is discussedto be much better in comparison to some other prior trends while system'smessage passing overload is decreased to less agents and the expertism helpsthe performance and operability of system win the comparison.

Object Classification by means of Multi-Feature Concept Learning in a Multi Expert-Agent System