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To appear in Computational Learning and Probabilistic Reasoning, A. Gammerman (Ed.), New York: Wiley. This is a revised version of ?Learning and reasoning as compression by multiple alignment and unification? presented at Applied Decision Technologies ?95, Brunel University, April 1995. A copy of that paper is in the Proceedings for Stream 1 (Computational Learning and Probabilistic Reasoning), pp. 223-236.

LEARNING AND REASONING AS

INFORMATION COMPRESSION BY

MULTIPLE ALIGNMENT, UNIFICATION

AND SEARCH

J Gerard Wolff

School of Electronic Engineering and Computer Systems, University of Wales, Dean

Street, Bangor, LL57 1UT, UK. Telephone +44 248 382691. E-mail:

gerry@sees.bangor.ac.uk. Fax: +44 248 361429. Web: http://

www.sees.bangor.ac.uk/~gerry.

September 1995

1 INTRODUCTION

This article presents the tentative idea that ?multiple alignment? in a sense which is close to the use of that term in bio-informatics, together with the full or partial merging or ?unification?1 of patterns, and a process of ?search?, is a framework within which learning and reasoning may be integrated.

This thinking is part of a programme of research aiming to develop the ?SP? conjecture (?computing as compression?) that all kinds of computing and formal reasoning may usefully be understood as information compression by pattern matching, unification and search (PMUS), and to develop a ?new generation? computing system based on the theory [19, 20, 21, 24].

Learning and reasoning are both large subjects. In the space of one short article it is not possible to do more than present a few examples to suggest how these things may be seen in terms of multiple alignment, unification and search. Relevant issues will be discussed more fully elsewhere.

Research on ?inductive logic programming? (ILP) is also concerned with learning and reasoning but the focus is different from the SP programme. In ILP (see, for example, [12]) the emphasis is on (supervised) learning within a framework of logic, whereas the SP programme seeks to integrate (unsupervised) learning and reasoning (and other aspects of computing) within a more

1. The term unification is used in this article to mean a simple merging of multiple instances of any pattern to make one. This idea is related to, but simpler than, the concept of ?unification? as it is used in logic.