Date of Award

2018

Degree Type

Dissertation

Degree Name

Doctor of Philosophy in Computer Science

Department

Computer Science and Stastistics

First Advisor

Lutz Hamel

Abstract

Inductive Logic Programming (ILP) is an area of research that is at the intersection of Machine Learning and Logic Programming. An ILP system uses positive and negative facts (examples) and optional background knowledge to induce a logic program that 1) accurately describes the facts and 2) successfully predicts the outcome of unseen examples.

This thesis introduces a new ILP algorithm implemented in Equational Logic that takes a hybrid approach to induction, using bottom-up generalization combined with inverse narrowing to create recursive equations.

We also introduce a framework for the induction of conditional equations from positive ground examples.

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