Interactive Structural Learning Of Bayesian Networks

We propose an hybrid approach for structure learning of Bayesian networks, in which a computer system and a human expert cooperate to search for the best structure. The system builds an initial tree structure which is graphically presented to the expert, and then the expert can modify this structure according to his knowledge of the domain. The system has several tools for aiding the human in this task: it allows for the graphical editing (adding, deleting, inverting arcs) of the network, it shows graphically the correlation between variables, and it gives a measure of the quality and complexity for each structure. We have tested the tool in two domains: atmospheric pollution and car insurance, with good results. I. INTRODUCTION The ability to learn is considered as one of the essential attributes of an "intelligent system", and a considerable amount of research has been done in this area. Learning can be defined as "any process by which a system improves its performance" [ 12 ] . The..