Theme One Program • Exposition 1

Theme One is a program for constructing and transforming a particular species of graph‑theoretic data structures, forms designed to support a variety of fundamental learning and reasoning tasks.

The program evolved over the course of an exploration into the integration of contrasting types of activities involved in learning and reasoning, especially the types of algorithms and data structures capable of supporting all sorts of inquiry processes, from everyday problem solving to scientific investigation.  In its current state, Theme One integrates over a common data structure fundamental algorithms for one type of inductive learning and one type of deductive reasoning.

We begin by describing the class of graph‑theoretic data structures used by the program, as determined by their local and global features.  It will be the usual practice to shift around and view these graphs at many different levels of detail, from their abstract definition to their concrete implementation, and many points in between.

The main work of the Theme One program is achieved by building and transforming a single species of graph‑theoretic data structures.  In their abstract form these structures are closely related to the graphs called cacti and conifers in graph theory, so we’ll generally refer to them under those names.

The Idea↑Form Flag

The graph‑theoretic data structures used by the program are built up from a basic data structure called an idea‑form flag.  That structure is defined as a pair of Pascal data types by means of the following specifications.

Type Idea = ^Form

  • An idea is a pointer to a form.
  • A form is a record consisting of:
    • A sign of type char;
    • Four pointers, as, up, on, by, of type idea;
    • A code of type numb, that is, an integer in [0, max integer].

Represented in terms of digraphs, or directed graphs, the combination of an idea pointer and a form record is most easily pictured as an arc, or directed edge, leading to a node labeled with the other data, in this case, a letter and a number.

At the roughest but quickest level of detail, an idea of a form can be drawn as follows.

Idea^Form Node

When it is necessary to fill in more detail, the following schematic pattern can be used.

Idea^Form Flag

The idea‑form type definition determines the local structure of the whole host of graphs used by the program, including a motley array of ephemeral buffers, temporary scratch lists, and other graph‑theoretic data structures used for their transient utilities at specific points in the program.

I will put off discussing the more incidental graph structures until the points where they actually arise, focusing here on the particular varieties of cactoid graphs making up the main formal media of the program’s operation.

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Theme One Program • Motivation 6

Comments I made in reply to a correspondent’s questions about delimiters and tokenizing in the Learner module may be worth sharing here.

In one of the projects I submitted toward a Master’s in psychology I used the Theme One program to analyze samples of data from my advisor’s funded research study on family dynamics.  In one phase of the study observers viewed video‑taped sessions of family members (parent and child) interacting in various modes (“play” or “work”) and coded qualitative features of each moment’s activity over a period of time.

The following page describes the application in more detail and reflects on its implications for the conduct of scientific inquiry in general.

In this application a “phrase” or “string” is a fixed‑length sequence of qualitative features and a “clause” or “strand” is a sequence of such phrases delimited by what the observer judges to be a significant pause in the action.

In the qualitative research phases of the study one is simply attempting to discern any significant or recurring patterns in the data one possibly can.

In this case the observers tokenize their observations according to a codebook which has passed enough intercoder reliability studies to afford a measure of confidence it captures meaningful aspects of whatever reality is passing before their eyes and ears.

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Theme One Program • Motivation 5

Since I’m working from decades‑old memories of first inklings I thought I might peruse the web for current information about Zipf’s Law.  I see there is now something called the Zipf–Mandelbrot (and sometimes –Pareto) Law and that was interesting because my wife Susan Awbrey made use of Mandelbrot’s ideas about self‑similarity in her dissertation and communicated with him about it.  So there’s more to read up on.

Just off‑hand, though, I think my Learner is dealing with a different problem.  It has more to do with the savings in effort a learner gets by anticipating future experiences based on its record of past experiences than the savings it gets by minimizing bits of storage as far as mechanically possible.  There is still a type of compression involved but it’s more like Korzybski’s “time‑binding” than space‑savings proper.  Speaking of old memories …

The other difference I see is that Zipf’s Law applies to an established and preferably large corpus of linguistic material, while my Learner has to start from scratch, accumulating experience over time, making the best of whatever data it has at the outset and every moment thereafter.

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Theme One Program • Motivation 4

From Zipf’s Law and the category of “things that vary inversely with frequency” I got my first brush with the idea that keeping track of usage frequencies is part and parcel of building efficient codes.

In its first application the environment the Learner has to learn is the usage behavior of its user, as given by finite sequences of characters from a finite alphabet, which sequences of characters might as well be called “words”, together with finite sequences of those words which might as well be called “phrases” or “sentences”.  In other words, Job One for the Learner is the job of constructing a “user model”.

In that frame of mind we are not seeking anything so grand as a Universal Induction Algorithm but simply looking for any approach to give us a leg up, complexity wise, in Interactive Real Time.

Resources

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Theme One Program • Motivation 3

Sometime around 1970 John B. Eulenberg came from Stanford to direct Michigan State’s Artificial Language Lab, where I would come to spend many interesting hours hanging out all through the 70s and 80s.  Along with its research program the lab did a lot of work on augmentative communication technology for limited mobility users and the observations I made there prompted the first inklings of my Learner program.

Early in that period I visited John’s course in mathematical linguistics, which featured Laws of Form among its readings, along with the more standard fare of Wall, Chomsky, Jackendoff, and the Unified Science volume by Charles Morris which credited Peirce with pioneering the pragmatic theory of signs.  I learned about Zipf’s Law relating the lengths of codes to their usage frequencies and I named the earliest avatar of my Learner program XyPh, partly after Zipf and playing on the xylem and phloem of its tree data structures.

Resources

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Theme One Program • Motivation 2

A side‑effect of working on the Theme One program over the course of a decade was the measure of insight it gave me into the reasons why empiricists and rationalists have so much trouble understanding each other, even when those two styles of thinking inhabit the very same soul.

The way it came about was this.  The code from which the program is currently assembled initially came from two distinct programs, ones I developed in alternate years, at first only during the summers.

In the Learner program I sought to implement a Humean empiricist style of learning algorithm for the adaptive uptake of coded sequences of occurrences in the environment, say, as codified in a formal language.  I knew all the theorems from formal language theory telling how limited any such strategy must ultimately be in terms of its generative capacity, but I wanted to explore the boundaries of that capacity in concrete computational terms.

In the Modeler program I aimed to implement a variant of Peirce’s graphical syntax for propositional logic, making use of graph‑theoretic extensions I had developed over the previous decade.

As I mentioned, work on those two projects proceeded in a parallel series of fits and starts through interwoven summers for a number of years, until one day it dawned on me how the Learner, one of whose aliases was Index, could be put to work helping with sundry substitution tasks the Modeler needed to carry out.

So I began integrating the functions of the Learner and the Modeler, at first still working on the two component modules in an alternating manner, but devoting a portion of effort to amalgamating their principal data structures, bringing them into convergence with each other, and unifying them over a common basis.

Another round of seasons and many changes of mind and programming style, I arrived at a unified graph‑theoretic data structure, strung like a wire through the far‑flung pearls of my programmed wit.  But the pearls I polished in alternate years maintained their shine along axes of polarization whose grains remained skew in regard to each other.  To put it more plainly, the strategies I imagined were the smartest tricks to pull from the standpoint of optimizing the program’s performance on the Learning task I found the next year were the dumbest moves to pull from the standpoint of its performance on the Reasoning task.  I gradually came to appreciate that trade‑off as a discovery.

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Theme One Program • Motivation 1

The main idea behind the Theme One program is the efficient use of graph-theoretic data structures for the tasks of “learning” and “reasoning”.

I am thinking of learning in the sense of learning about an environment, in essence, gaining information about the nature of an environment and being able to apply the information acquired to a specific purpose.

Under the heading of reasoning I am simply lumping together all the ordinary sorts of practical activities which would probably occur to most people under that name.

There is a natural relation between the tasks.  Learning the character of an environment leads to the recognition of laws which govern the environment and making full use of that recognition requires the ability to reason logically about those laws in abstract terms.

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Precursors Of Category Theory • 6

Hilbert and Ackermann • Principles of Mathematical Logic (1928)

For the intuitive interpretation on which we have hitherto based the predicate calculus, it was essential that the sentences and predicates should be sharply differentiated from the individuals, which occur as the argument values of the predicates.  Now, however, there is nothing to prevent us from considering the predicates and sentences themselves as individuals which may serve as arguments of predicates.

Consider, for example, a logical expression of the form (x)(A \rightarrow F(x)).  This may be interpreted as a predicate P(A, F) whose first argument place is occupied by a sentence A, and whose second argument place is occupied by a monadic predicate F.

A false sentence A is related to every F by the relation P(A, F);   a true sentence A only to those F for which (x)F(x) holds.

Further examples are given by the properties of reflexivity, symmetry, and transitivity of dyadic predicates.  To these correspond three predicates:  \mathrm{Ref}(R), \mathrm{Sym}(R), and \mathrm{Tr}(R), whose argument R is a dyadic predicate.  These three properties are expressed in symbols as follows:

\begin{array}{l}  \mathrm{Ref}(R) \colon (x)R(x, x),  \\[6pt]  \mathrm{Sym}(R) \colon (x)(y)(R(x, y) \rightarrow R(y, x)),  \\[6pt]  \mathrm{Tr}(R) \colon (x)(y)(z)(R(x, y) \And R(y, z) \rightarrow R(x, z)).  \end{array}

All three properties are possessed by the predicate \equiv(x, y)   (x is identical with y).  The predicate <(x, y), on the other hand, possesses only the property of transitivity.  Thus the formulas \mathrm{Ref}(\equiv), \mathrm{Sym}(\equiv), \mathrm{Tr}(\equiv), and \mathrm{Tr}(<) are true sentences, whereas \mathrm{Ref}(<) and \mathrm{Sym}(<) are false.

Such predicates of predicates will be called predicates of second level.  (p. 135).

We have, first, predicates of individuals, and these are classified into predicates of different categories, or types, according to the number of their argument places.  Such predicates are called predicates of first level.

By a predicate of second level, we understand one whose argument places are occupied by names of individuals or by predicates of first level, where a predicate of first level must occur at least once as an argument.  The categories, or types, of predicates second level are differentiated according to the number and kind of their argument places.  (p. 152).

Reference

  • Hilbert, D. and Ackermann, W., Principles of Mathematical Logic, Robert E. Luce (trans.), Chelsea Publishing Company, New York, NY, 1950.  1st published, Grundzüge der Theoretischen Logik, 1928.  2nd edition, 1938.  English translation with revisions, corrections, and added notes by Robert E. Luce, 1950.

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Precursors Of Category Theory • 5

A demonstration rests in a finite number of steps.

G. Spencer Brown • Laws of Form

David Hilbert • “On the Infinite” (1925)

Finally, let us recall our real subject and, so far as the infinite is concerned, draw the balance of all our reflections.  The final result then is:  nowhere is the infinite realized;  it is neither present in nature nor admissible as a foundation in our rational thinking — a remarkable harmony between being and thought.  We gain a conviction that runs counter to the earlier endeavors of Frege and Dedekind, the conviction that, if scientific knowledge is to be possible, certain intuitive conceptions [Vorstellungen] and insights are indispensable;  logic alone does not suffice.  The right to operate with the infinite can be secured only by means of the finite.

The role that remains to the infinite is, rather, merely that of an idea — if, in accordance with Kant’s words, we understand by an idea a concept of reason that transcends all experience and through which the concrete is completed so as to form a totality — an idea, moreover, in which we may have unhesitating confidence within the framework furnished by the theory that I have sketched and advocated here.  (p. 392).

References

  • Hilbert, D. (1925), “On the Infinite”, pp. 369–392 in Jean van Heijenoort (1967/1977).
  • van Heijenoort, J. (1967/1977), From Frege to Gödel : A Source Book in Mathematical Logic, 1879–1931, Harvard University Press, Cambridge, MA, 1967. 2nd printing, 1972. 3rd printing, 1977.
  • Spencer Brown, G. (1969), Laws of Form, George Allen and Unwin, London, p. 54.

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Precursors Of Category Theory • 4

C.S. Peirce • “Prolegomena to an Apology for Pragmaticism” (1906)

I will now say a few words about what you have called Categories, but for which I prefer the designation Predicaments, and which you have explained as predicates of predicates.

That wonderful operation of hypostatic abstraction by which we seem to create entia rationis that are, nevertheless, sometimes real, furnishes us the means of turning predicates from being signs that we think or think through, into being subjects thought of.  We thus think of the thought‑sign itself, making it the object of another thought‑sign.

Thereupon, we can repeat the operation of hypostatic abstraction, and from these second intentions derive third intentions.  Does this series proceed endlessly?  I think not.  What then are the characters of its different members?

My thoughts on this subject are not yet harvested.  I will only say that the subject concerns Logic, but that the divisions so obtained must not be confounded with the different Modes of Being:  Actuality, Possibility, Destiny (or Freedom from Destiny).

On the contrary, the succession of Predicates of Predicates is different in the different Modes of Being.  Meantime, it will be proper that in our system of diagrammatization we should provide for the division, whenever needed, of each of our three Universes of modes of reality into Realms for the different Predicaments.  (CP 4.549).

The first thing to extract from the above passage is that Peirce’s Categories, for which he uses the technical term “Predicaments”, are predicates of predicates.  Considerations of the order Peirce undertakes tend to generate hierarchies of subject matters, extending through what is traditionally called the logic of second intentions, or what is handled very roughly by second order logic in contemporary parlance, and continuing onward through higher intentions, or higher order logic and type theory.

Peirce arrived at his own system of three categories after a thoroughgoing study of his predecessors, with special reference to the categories of Aristotle, Kant, and Hegel.  The names he used for his own categories varied with context and occasion, but ranged from moderately intuitive terms like quality, reaction, and symbolization to maximally abstract terms like firstness, secondness, and thirdness.  Taken in full generality, k‑ness may be understood as referring to those properties all k‑adic relations have in common.  Peirce’s distinctive claim is that a type hierarchy of three levels is generative of all we need in logic.

Part of the justification for Peirce’s claim that three categories are necessary and sufficient appears to arise from mathematical facts about the reducibility of k‑adic relations.  With regard to necessity, triadic relations cannot be completely analyzed in terms or monadic and dyadic predicates.  With regard to sufficiency, all higher arity k‑adic relations can be analyzed in terms of triadic and lower arity relations.

Reference

  • Peirce, C.S. (1906), “Prolegomena to an Apology for Pragmaticism”, The Monist 16, 492–546, CP 4.530–572.

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