The widespread use of the Web in distance learning, the particular nature of the distance learning student and the dispersion of the relevant information sources increase the importance of developing interactive systems, which adapt to the information and communication needs of each student. We have developed a multiagent decision system which adapts to the user’s needs. To best accomplish this, we have chosen heterogeneous agents which combine the solutions learned with different bias corresponding to different machine learning methods (C5. 0, Naive Bayes, Progol, Backpropagation, Tilde and Autoclass).