[26 March 2010]
Long ago I used a wonderful file-oriented database system called Watfile, myocarditis which was designed as a sort of desk-calculator for data. It was designed for personal use, prostate not industrial-strength data management, order and its designers successfully resisted the temptation to add more features and more power at the cost of a more complex user interface. Watfile was to a full enterprise-class database as a desk calculator of the 1960s was to … oh, perhaps to Fortran. For suitable problems, the ease of setup far outweighed any considerations of power or completeness.
The experience of using Watfile for data manipulation tasks established in my mind the class of ‘desk-calculator-like’ packages for various kinds of problem.
Today I experimented with Alloy as a sort of logical desk calculator, and I’m happy to report that it passed the test with flying colors.
For reasons I won’t go into here, I’ve wondered a bit recently what it might look like to apply the technique of distinctive-feature analysis (originally developed for phonological descriptions of sound systems of language) to writing systems. When I sat down a few months ago with pencil and paper to see if I could devise a smallish set of typographic features which could (say) distinguish the twenty-six letters of the alphabet as I was taught it in first grade, I rapidly found that working solely with pen and paper made me impatient: it was too tedious to look at the set of features already identified and see which letters could not yet be distinguished (because they had the same value for all the features in question).
When I came back to this problem this afternoon, I thought for a few minutes about what questions I’d like to be able to ask the machine. Given a specified set of graphemes (as a first exercise, I chose the lower-case alphabet) and a specified set of binary features (does the letter have an ascender? a descender? a vertical stroke? a full or partial circle or bowl? Is the stroke to the left of the bowl? …), with information about which graphemes have the feature in question, I want to be able to ask, first, whether a particular set of features suffices to distinguish each individual grapheme? Or are there two or more graphemes which have the same value for all features in the set? And of course, if there are such sets of indistinct graphemes, what are they?
It occurred to me to solve the problem in Prolog; it would take just a relatively simple set of Prolog predicates to do what I wanted. But as I was preparing to launch X Windows, so that I could launch Prolog, I realized that I already had the Alloy Analyzer running. And so I wrote the predicates I wanted in Alloy instead of Prolog, to see whether it would work.
The upshot is: yes, it worked, and it was probably a bit easier to do than it would have been in Prolog. When I was thinking about how to set up the problem in Prolog, I found myself wondering about the best data representation to choose, and so on, almost as much as about the structure of the problem. I won’t say that Alloy posed no analogous problems — I did have to think for a moment or two about the best way to describe graphemes and distinctive features. But the high level of abstraction afforded by Alloy made the decision feel less binding, and made me feel a bit more comfortable experimenting. (It sounds strange to put it this way: after all, Prolog’s high level of abstraction is one of its great design strengths. But Prolog is also designed to be an efficient and effective programming language, which means that some details are exposed which have only procedural significance, and sometimes you find yourself thinking about them, even in situations where questions of execution efficiency don’t arise.
In very short order, I found it possible to define a suitably abstract representation of graphemes and features, specify some useful functions and predicates for asking the questions described above, and specify a small set of features (ten) which have a certain degree of typographic plausibility and which suffice to distinguish the graphemes in question. (Ten binary features for twenty-six graphemes may seem high, given that the theoretical minimum is only five, and that ten bits suffice to distinguish a thousand objects, not just twenty-six. But writing, like natural language, has some redundancy. Feature sets used to analyse natural language sound systems are also often very inefficient.) The visualization tools did not prove very helpful, but the Evaluator feature of the Alloy Analyzer was a great help.
If I pursue this work any further, I probably will put it into Prolog, where the interactive interface for expression evaluation is perhaps a bit more convenient than in Alloy. But it’s nice to know that Alloy can be used for this kind of problem, too.
Interested readers can find both the generic definitions and the specific graphemes and features for lower-case Latin letters (as used in Anglophone countries) on the Black Mesa Technologies web site.