City Research Online

Abstract

The learning of rules from examples is of continuing interest to machine learning since it allows generalization from fewer training ex- amples. Inductive Logic Programming (ILP) generates hypothetical rules (clauses) from a knowledge base augmented with (positive and negative) examples. A successful hypothesis entails all positive examples and does not entail any negative example. The Shared Neural Multi-Space (Shared NeMuS) structure encodes first order expressions in a graph suitable for ILP-style learning. This paper explores the NeMuS structure and its re- lationship with the Herbrand Base of a knowledge-base to generate hy- potheses inductively. It is demonstrated that inductive learning driven by the knowledge-base structure can be implementated successfully in the Amao cognitive agent framework, including the learning of recursive hypotheses.

Publication Type:	Article
Additional Information:	Copyright © 2017 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.
Departments:	School of Science & Technology > Computer Science

Export

Downloads