To solve a complex problem, one of the effective general approaches is to decompose it into smaller, less complex and more manageable subproblems. In machine learning, this principle is a foundation for structured induction [44]: instead of learning a single complex classification rule from examples, define a concept hierarchy and learn rules for each of the (sub)concepts. Shapiro [44] used structured induction for the classification of a fairly complex chess endgame and demonstrated that the complexity and comprehensiveness (“brain-compatibility”) of the obtained solution was superior to the unstructured one. Shapiro was helped by a chess master to structure his problem domain. Typically, applications of structured induction involve a manual development of the hierarchy and a manual selection and classification of examples to induce the subconcept classification rules; usually this is a tiresome process that requires an active availability of a domain expert over long periods of time. Therefore, it would be very desirable to automate the problem decomposition task.