An approach to problem-oriented learning of a multi-agent system is proposed. The corresponding methods apply a kind of such a notion as knowledge and the introduced notion of φ-linear models that are a specific class of general nonlinear ones. Specifically, the values of transfer coefficients were shown to be adapted to the solved identification problem and are in sequel used by the multi-agent system to form the problem-oriented knowledge about the object under study, and updating the knowledge base.

The paper presents methods to analyze approaches concerned with application of information theoretic techniques in such a branch of the control theory as system identification: application of the mutual (Shannon) information and attempts of generalization of the notion of entropy, as well as application of consistent measures of dependence based on the information-theoretic (Kulback-Leibler) divergence in system identification. Ways and methods, both analytical and simulation ones are presented. The paper has been supported by a grant of the Russian Foundation for Basic Researches (RFBR): project 12-08-01205-a.