Induction of logical relations based on specific generalization of strings

Abstract

Learning logical relations from examples expressed as first order facts has been studied extensively by the Inductive Logic Programming research. Learning with positive-only data may cause overgeneralization of examples leading to inconsistent resulting hypotheses. A learning heuristic inferring specific generalization of strings based on unique match sequences is shown to be capable of learning predicates with string arguments. This thesis outlines the effort showed to build an inductive learner based on the idea of specific generalization of strings that generalizes given clauses considering the background knowledge using least general generalization schema. The system is also extended to generalize predicates having numeric arguments and shown to be capable of learning concepts such as family relations, grammar learning and predicting mutagenecity using numeric data.