Learning Abductive Theories

In this paper we study the problem of learning abductive theories with particular interest in learning theories for the problem of attribute-based classification as studied in the area of machine learning. The paper proposes a new alternative formulation of this class of learning problems where abduction takes an integral part in the formulation of the appropriate theories and more importantly in the definition of the learning problem itself. We present a general algorithm that learns abductive theories for classificationand examine its main features. We show how within our abductive approach it is possible to formulate and handle in a natural way cases of the problem with incomplete information. We also study the relation of our approach to other existing approachesfor these learning problems, notably that of decision trees, and argue that our approach could provide a useful link between abduction and machine learning.