Cautious Induction in Inductive Logic Programming

. Many top-down Inductive Logic Programming systems use a greedy, covering approach to construct hypotheses. This paper presents an alternative, cautious approach, known as cautious induction. We conjecture that cautious induction can allow better hypotheses to be found, with respect to some hypothesis quality criteria. This conjecture is supported by the presentation of an algorithm called CILS, and with a complexity analysis and empirical comparison of CILS with the Progol system. The results are encouraging and demonstrate the applicability of cautious induction to problems with noisy datasets, and to problems which require large, complex hypotheses to be learnt. 1 Introduction Within the Inductive Logic Programming (ILP) paradigm [4, 2], many of the topdown (or refinement-based) systems, such as FOIL [5] and Progol [3], employ a greedy, covering approach to the construction of a hypothesis. Informally, from the training sample and background knowledge, the learner searches a spa..