Artificial Intelligence is a methodological mess: a surfeit of programs and a dearth of justified theories and principles. Typically we have a large and complex program that exhibits some interesting behaviours. The underlying principles are anybody's guess.

In addition, if principles are presented, the gulf between program and principle is sufficient to preclude any systematic discussion of the program-principle relationship. We must do more than just present a principle.

A methodology for abstracting and justifying the principles that underlie an AI program is explained and demonstrated. The viewpoint taken is that we cannot prove that a program embodies a given principle; we can only make a claim to this effect with an explicit supporting argument, and thereby provide a concrete basis for discussion of the credibility of the putative principle.

Introduction

AI is largely a behavioural science: a science that is founded upon the behaviour of programs. The working program embodies the theory or set of principles.

Computer programs are very persuasive arguments for the theory that they model. They are also largely incomprehensible to anyone but (or including?) their author. Hence whilst the credibility of the theory is founded on the program the theory, presented perhaps in terms of principles, is necessarily couched in the vagaries and generalizations of the English language; the relationship between the working program and the comprehensible principles can only be founded on faith. We need something better than this.