“A structure from manipulation for text-graphic objects” by Lakin

The general purpose graphics systems of the future will need a simple logic for visual objects—one structure underlying both text and graphics. As an experiment, perhaps the immediate handling of visual objects by the user can provide the starting point for developing that structure. This paper describes the PAM graphics system, in which the structure of text-graphic objects arises directly out of manual manipulation. The needs of manual manipulation determine the text-graphic pattern as the simplest organizing structure for images; PAM stands for PAttern Manipulating. The PAM system is designed for the agile manipulation of text-graphic patterns—first manually, and then, later, programmatically. Starting from this strict ‘front-in’ viewpoint—where immediate manipulation (hand powered animation) was to be the primary application—a ‘manipulative grammar’ was evolved to give the user a simple yet powerful handle on text-graphic images. This grammar turned out to be a generalization of LISP syntax from textual symbolic expressions to text-graphic forms, structuring such forms as trees and then offering: @@@@spatial GRABBING of objects into attention @@@@tree guided attention shifters like FIRST, REST, NEXT, and UP spatial & tree manipulations on any object in attention. @@@@The resulting structures also offer surprising computational power (in a manner directly analogous to the way the basic list structures and functions of LISP give rise to the flexibility and power of a full blown LISP system, McCarthy and Talcott [1]), leading finally to computing with text-graphic forms. Consequently, a semantic function is added to supplement the basic manipulative grammar: @@@@evaluation of object in attention, result displayed at the cursor. Evaluation supports facilities like naming (and thus saving) visual objects, programming, and creation of menus (patterns of evaluatable function objects). An experimental version of the PAM system has been implemented in MACLISP at the Stanford Artificial Intelligence Lab.

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