Guiding induction with domain theories

Abstract In this chapter we present a concept-acquisition methodology that uses data (concept examples and counterexamples), domain knowledge, and tentative concept descriptions in an integrated way. Domain knowledge can be incomplete and/or incorrect with respect to the given data; moreover, the tentative concept descriptions can be expressed in a form that is not operational. The methodology is aimed at producing discriminant and operational concept descriptions, by integrating inductive and deductive learning. In fact, the domain theory is used in a deductive process, that tries to operationalize the tentative concept descriptions, but the obtained results are tested on the whole learning set rather than on a single example. Moreover, deduction is interleaved with the application of data-driven inductive steps. In this way, a search in a constrained space of possible descriptions can help overcome some limitations of the domain theory (e.g., inconsistency). The method has been tested in the framework of the inductive learning system “ML-SMART,” previously developed by the authors, and a simple example is also given.