Stochastic Inductive Logic Programming

Abstract

Machine learning is an important part of artificial intelligence and its applications. Learning from instances is one of the most active areas within machine learning. Initial successes in the induction of propositional theories have been followed by algorithms that construct hypotheses in the form of (a subset of) the first order relational concepts. Such learning is called Inductive Logic Programming (ILP). This thesis deals with two key problems of machine learning of concepts from instances: hypothesis justification and hypothesis construction which are also a vital part of the form of reasoning called inductive inference. The purpose of concept formation is information compression (a hypothesis describes or "explains" given learning instances). Generally, there is not a single hypothesis that explains the given instances of the target concept. One criterion for hypothesis justification is based on the Minimum description length (MDL) principle which states that a hypothesis which..