Year: 2018
Type: dissertation
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.
Author: McDonald, Arthur A.
Institution: University of Rhode Island
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2903355779