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The return benefits to systems theory would be equally valuable, enabling the implementation of more intelligent software for the study of complex systems. The engineering of this software could extend work already begun in simulation modeling (Widman, Loparo, & Nielsen, 1989), (Yip, 1991), nonlinear dynamics and chaos (Rietman, 1989), (Tufillaro, Abbott, & Reilly, 1992), and expert systems (Bratko, Mozetic, & Lavrac, 1989), with increasing capabilities for qualitative inference about complex systems and for intelligent navigation of dynamic manifolds (Weld & de Kleer, 1990).

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~~<div class="nonumtoc">~~__TOC__~~</div>~~

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__TOC__

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==1. Background==

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==Background==

In my aim to connect the enterprises of systems theory and artificial intelligence I recognize the following facts. Although the control systems approach was a prevailing one in the early years of cybernetics and important tributaries of AI have sprung from its sources, e.g. (Ashby, 1956), (Arbib, 1964, '72, '87, '89), (Albus, 1981), the two disciplines have been pursuing their separate evolutions for many years now. The intended scope of AI, overly ambitious or otherwise, forced it to break free of early bonds, shifting for itself beyond the orbit of its initial paradigms and the cases that conditioned its origin.

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There are harvests of complexity which sprout from the earliest elements and the simplest levels of the discussion that follows. I will try to clarify a few of these issues in the process of fixing terminology. This may create an impression of making much ado about nothing, but it is a good idea in computational modeling to forge connections between the complex, the subtle, and the simple -- even to the point of forcing things a bit. Further, I will use this space to profile the character and the consistency of the grounds being tended by systems theory and AI. Finally, I will let myself be free to mention features of this work that connect with the broader horizons of human cultivation. Although these concerns are properly outside the range of my next few steps, I believe that it is important to be aware of our bearings: to know what our practice depends upon, to think what our activity impacts upon.

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===~~1.1.~~ Topos : Rudiments and Immediate Resources===

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===Topos : Rudiments and Immediate Resources===

This inquiry is guided by two questions that express themselves in many different guises. In their most laconic and provocative style, self-referent but not purely so, they typically bring a person to ask:

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====~~1.1.1.~~ Systematic Inquiry====

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====Systematic Inquiry====

In their underlying form and tone these questions sound a familiar tune. Their basic tenor was brought to a pitch of perfection by Immanuel Kant, in a canon of inquiry that exceeds my present range. Luckily, my immediate aim is much more limited and concrete. For the present it is only required to ask: ''How are systematic inquiry and knowledge possible?'' That is, how are inquiry and knowledge to be understood and implemented as functions of systems and how ought they be investigated by systems theory? In short: ''How can systems have knowledge as a goal?'' This effort is constrained to the subject of systems and the frame of systems theory. It will attempt to give system-theoretic analyses of concepts and capacities that can be recognized as primitive archetypes, at least, of those that AI research pursues with avid interest and aspires one day to more fully capture. By limiting questions about the possibility of inquiry and knowledge to the subject and scope of systems theory there may be reason to hope for a measure of practical success.

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Kant's challenge is this: To say precisely ''how'' it is possible, in procedural terms, for contingent beings and empirical creatures, physically embodied and even engineered systems, to move toward or synthetically acquire forms of knowledge with an ''a priori'' character, that is, declarative statements with a global application to all of the situations that these agents might pass through. It is not feasible within the scope of systems theory and engineered systems to deal with the larger question: Whether these forms of knowledge are somehow ''necessary'' laws, applying to all conceivable systems and universes. But it does seem reasonable to ask how a system's trajectory might intersect with states whose associated knowledge components have a wider application to the system's manifold as a whole.

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====~~1.1.2.~~ Intelligence, Knowledge, Execution====

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====Intelligence, Knowledge, Execution====

Intelligence, for my purposes, is characterized as a technical ability of choice in a situation as represented. It is the ability to pick out a line on a map, to find a series of middle terms making connections between represented positions. In the situation that commonly calls it out, intelligence is faced with two representations of position. This pair of pointers to points on a map are typically interpreted as indices of current and desired positions. The two images are symbols or analogues of the actual site and the intended goal of a system. They themselves exist in a space that shadows the dynamic reality of the agent involved. But the dynamic reality of the intelligent agent forms a manifold of states that subsists beneath its experience and becomes manifest

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The concept of intelligence laid out here has been abstracted from two capacities that it both requires and supports: knowledge and execution. Knowledge is a fund of available representations, a glove-box full of maps. Execution is an array of possible actions and the power of performing them, an executive ability that directs motor responses in accord with the line that is picked out on the map. To continue the metaphor, execution is associated with the driving-gloves, which must be sorted out from the jumble of maps and used to get a grip on the mechanisms of performance and control that are capable of serving in order to actualize choices.

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=====~~1.1.2.1.~~ Vector Field and Control System=====

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=====Vector Field and Control System=====

Dynamically, as in a control system, intelligence is a decision process that selects an indicator of a tangent vector to follow at a point or a descriptor of a corresponding operator to apply at a point. The pointwise indicators or descriptors can be any relevant signs or symbolic expressions: names, code numbers, address pointers, or quoted phrases. A "vector field" attaches to each point of phase space a single tangent vector or differential operator. The "control system" is viewed as a ready generalization of a vector field, in which whole sets of tangent vectors or differential operators are attached to each point of phase space. The "strategy" or "policy problem" of a controller is to pick out one of these vectors to actualize at each point in accord with reaching a given target or satisfying a given property. An individual control system is specified by information attached to each dynamic point that defines a subset of the tangent space at that point. This pointwise defined subset is called "the indicatrix of permissible velocities" by (Arnold, 1986, chapt. 11).

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In the usage needed for combining AI and control systems to obtain autonomous intelligent systems, it is important to recognize that the pointwise indicators and descriptors must eventually have the character of symbolic expressions existing in a language of non-trivial complexity. Relating to this purpose, it does not really matter if their information is viewed as represented in the states of discrete machines or in the states of physical systems to which real and complex valued measurements are attributed. What makes the system of indications and descriptions into a language is that its elements obey specific sets of axioms that come to be recognized as characterizing interesting classes of symbol systems. Later on I will indicate one very broad definition of signs and symbol systems that I favor. I find that this conception of signs and languages equips the discussion of intelligent systems with an indispensable handle on the levels of complexity that arise in their description, analysis, and clarification.

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=====~~1.1.2.2.~~ Fields of Information and Knowledge=====

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=====Fields of Information and Knowledge=====

Successive extensions of the vector field concept can be achieved by generalizing the form of pointwise information defined on a phase space. A subset of a tangent space at a point can be viewed as a boolean-valued function there, and as such can be generalized to a probability distribution that is defined on the tangent space at that point. This type of probabilistic vector field or "information field" founds the subject of stochastic differential geometry and its associated dynamic systems. An alternate development in this spirit might embody pointwise information about tangent vectors in the form of linguistic expressions and ultimately in knowledge bases with the character of empirical summaries or logical theories attached to each point of a phase space.

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It is convenient to bring together under the heading of a "knowledge field" any form of pointwise information, symbolic or numerical, concrete or theoretical, that constrains the set of pointwise tangent vectors defined on a phase space. In computational settings this information can be procedural and declarative program code augmented by statistical and qualitative data. In computing applications a knowledge field acquires an aptly suggestive visual image: bits and pieces of code and data elements sprinkled on a dynamic surface, like bread crumbs to be followed through a forest. The rewards and dangers of so literally a "distributed" manner of information storage are extremely well-documented (Hansel & Gretel, n.d.), but there are times when it provides the only means available.

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=====~~1.1.2.3.~~ The Trees, The Forest=====

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=====The Trees, The Forest=====

A sticking point of the whole discussion has just been reached. In the idyllic setting of a knowledge field the question of systematic inquiry takes on the following form:

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Questions like these are only ways of probing the range of possible systems that are implied by the definition of a knowledge field. What abstract possibility best describes a given concrete system is a separate, empirical question. With luck, the human situation will be found among the reasonably learnable universes, but before that hope can be evaluated a lot remains to be discovered about what, in fact, may be learnable and reasonable.

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====~~1.1.3.~~ Reality and Representation====

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====Reality and Representation====

A sidelight that arose in the characterization of intelligence is recapitulated here. Beginning with experience described in phenomenal terms, the possibility of objective knowledge appears to depend on a certain factorization or decomposition of the total manifold of experience into a pair of factors: a fundamental, original, objective, or base factor and a representational, derivative, subjective, or free factor. To anticipate language that will be settled on later, the total manifold of phenomenal experience is said to factor into a bundle of fibers. The bundle structure corresponds to the base factor and the fiber structure corresponds to the free factor of the decomposition. Fundamental definitions and theorems with respect to fiber bundles are given in (Auslander & MacKenzie, ch. 9). Discussions of fiber bundles in physical settings are found in (Burke, p. 84-108) and (Schutz, 1980). Concepts of differential geometry directed toward applications in control engineering are treated in (Doolin & Martin, ch. 8). An ongoing project in AI that uses simple aspects of fiber methods to build cognitive models of physics comprehension is described in (Bundy & Byrd, 1983).

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The most fitting factorization is not necessarily given in advance, though any number of possibilities may be tried out initially. The most suitable distinction between phenomenal reality and epiphenomenal representation can be a matter determined by empirical or pragmatic factors. Of course, with any empirical investigation there can be logical and mathematical features that place strong constraints on what is conceivably possible, but the risk remains that the proper articulation may have to be discovered through empirical inquiry carried on by a systematic agent delving into its own world of states without absolutely dependable lines as guides. The appropriate factorization, ideally the first item of description, may indeed be the chief thing to find out about a system and the principal thing to know about the total space of phenomena it manifests, and yet persist in being the last fact to be fully settled.

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=====~~1.1.3.1.~~ Levels of Analysis=====

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=====Levels of Analysis=====

The primary factorization is typically only the first in a series of analytic decompositions that are needed to fully describe a complex domain of phenomena. The question about proper factorization that this discussion has been at pains to point out becomes compounded into a question about the reality of all the various distinctions of analytic order. Do the postulated levels really exist in nature, or do they arise only as the artifacts of our attempts to mine the ore of nature? An early appreciation of the hypothetical character of these distinctions and the post hoc manner of their validation is recorded in (Chomsky, 1975, p. 100).

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The language of category theory preserves a certain idiom to express this aspect of facticity in phenomena (MacLane, 1971), which incidentally has impacted the applied world in the notions of a database view (Kerschberg, 1986) and a simulation viewpoint (Widman, Loparo, & Nielsen, 1989). In this usage a level constitutes a functor, that is, a particular way of viewing a whole category of objects under study. For direct applications of category theory to abstract data structures, computable functions, and machine dynamics see (Arbib & Manes, 1975), (Barr & Wells, 1985, 1990), (Ehrig, et al., 1985), (Lambek & Scott, 1986), and (Manes & Arbib, 1986). A proposal to extend the machinery of category theory from functional to relational calculi is developed in (Freyd & Scedrov, 1990).

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=====~~1.1.3.2.~~ Base Space and Free Space=====

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=====Base Space and Free Space=====

The base space is intended to capture the fundamental dynamic properties of a system, those aspects to which the other dynamic properties may be related as derivative quantities, free parameters, or secondary perturbances. The remainder consists of tangential features. For simple physical systems this second component contains derivative properties, like velocity and momentum, that are represented as elements of pointwise tangent spaces. In an empirical sense these features do not properly belong to a single point but are attributed to a point on account of measurements made over several points. Of course, from the dual perspective it is momentum that is real and position that is illusion, but that does not affect the point in question, which concerns the uncertainty of their discernment, not the fact of their complementarity.

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=====~~1.1.3.3.~~ Unabridgements=====

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=====Unabridgements=====

Part of my task in the projected work is to make a bridge, in theory and practice, from simple physical systems to the more complex systems, also physical but in which new orders of features have become salient, that begin to exhibit what is recognized as intelligence. At the moment it seems that a good way to do this is to anchor the knowledge component of intelligent systems in the tangent and co-tangent spaces that are founded on the base space of a dynamic manifold. This means finding a place for knowledge representations in the residual part of the initial factorization. This leads to a consideration of the questions: What makes the difference between these supposedly different factors of the total manifold? What properties mark the distinction as commonly

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From a naturalistic perspective everything falls equally under the prospective heading of ''physis'', signifying nothing more than the first inklings of natural process, though not everything is necessarily best explained in detail by those fragments of natural law which are currently known to us. So it falls to any science that pretends to draw a distinction between the more and the less basic physics to describe it within nature and without trying to get around nature. In this context the question may now be rephrased: What natural terms distinguish every system's basic processes from the kinds of coping processes that support and crown the intelligent system's personal copy of the world? What protocols attach to the sorting and binding of these two different books of nature? What colophon can impress the reader with a need to read them? What instinct can motivate a basis for needing to know?

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====~~1.1.4.~~ Components of Intelligence====

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====Components of Intelligence====

In a complex intelligent system a number of relatively independent modules will emerge as utilities to subserve the purpose of knowledge acquisition. Chief among these are the faculties of memory and imagination, which operate in closely coordinated representation spaces of the manifold, and may be no more than complementary ways of managing the same turf. These capacities amplify the sensitivity and selectivity of intelligence in the system. They support the transcription of momentary experience into records of its passing. Finally, they collate the fragmentary notes and diverse notations of dynamic experience and catalyze their conversion into unified forms and organizations of rational knowledge.

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=====~~1.1.4.1.~~ Imagination=====

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=====Imagination=====

The intellectual factor or knowledge component of a system is usually expected to have a certain quality of mercy, that is, to involve actions which are Reversible, Assuredly, Immediately, Nearly. Even though every action obeys physical and thermodynamic constraints, processes that suit themselves to being used for knowledge representation must exhibit a certain forgiveness. It must be possible to move pointers around on a map without irretrievably committing forces on the plain of battle. Actions carried out in the image space should not incur too great a pain or price in terms of the time and energy they dissipate. In sum, a virtue of symbolic operations is that they be as nearly and assuredly reversible as possible. This "virtual" construction, as usual, declares a positively oriented proportion: operations are useful as symbolic transformations in proportion to their exact and certain reversibility.

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Imagination's development of elaborate and seemingly superabundant resources of imagery is actually governed by strict obedience to the cybernetic law of requisite variety, which determines that only variety in the responses of a regulator can counter the flow of variety from disturbances to essential variables, the qualities the system must act to keep nearly constant in order to survive in its current and preferred form of being (Ashby, ch. 10 & 11). Aristotle, thinking that the human brain was too flimsy and spongy a material to embody the human intellect, thought it might be useful as a kind of radiator to cool the blood. This is actually a pretty good theory, I think, if it is recognized that the specialty of the brain is to regulate essential variables of human existence on a global scale through the discovery of natural laws. To view the brain as a theorem-o-stat is then fairly close to the mark.

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=====~~1.1.4.2.~~ Remembrance=====

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=====Remembrance=====

The purpose of memory, on the other hand, requires states that can be duly constituted in fashions that are diligently preserved by the normal functioning of the system. The expectation must be faithfully met that such states will be maintained until deliberately reformed by due processes. Intelligent systems cannot afford to indiscriminately confound the imperatives to forgive and forget. Reversibility applies to exploratory operations taking place interior to the dynamic image. An irreversible recording of events is generally the best routine strategy to keep in play between outer and inner dynamics. But reversibility and its opposite interact in subtle ways even to maintain the stability of stored representations. After all, when physical records are disturbed by extraneous noise without the mediation of attention's due process, the ideal system would work to immediately reverse these unintentional distortions and ungraceful degradations of its memories. To abide their time, memories should lie in state spaces with stable equilibria, resting at the bottoms of deep enough potential wells to avoid being tossed out by minor quakes.

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in (Eco, 1983).

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===~~1.2.~~ Hodos : Methods, Media, and Middle Courses===

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===Hodos : Methods, Media, and Middle Courses===

To every thing there is a season. To every concept there are minimal contexts of sensible application, the most reduced situations in which the concept can be used to make sense. Every concept is an instrument of thought, and like every method has its bounds of useful application. In directions of simplicity, a concept is bounded by the minimum levels of complexity out of which it is, initially, recurrently, transiently, ultimately, able to arise. There is one form of rhetorical question that people often use to address this issue, if somewhat indirectly. It presents itself initially as a genuine question but precipitates the answer in enthymeme, dashing headlong to break off inquiry in the form of a blank. Ostensibly, the space extends the question, but it is only a pretext. The pretense of an open sentence is already filled in by the unexpressed beliefs of the questioner.

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More carefully said, information is a property that can be attributed to signs in a system by virtue of their relation to two other systems. This attribution projects a relation among three domains into a simpler order of relation. There are various ways of carrying out this reduction. Not all of them can be expected to preserve the information of the original sign relation. An attribution may create a logical property of elements in the sign domain or it may construct functions from the sign domain to ranges of qualitative meaning or quantitative measure.

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====~~1.2.1.~~ Functions of Observation====

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====Functions of Observation====

An observation preserved in a permanent record marks the transience of a certain compound event, the one that an observational account creates in conjunction with the events leading up to it. If an observation succeeds in making an indelible record of an event, then a certain transient of the total system has been passed. To the extent that the record is a lasting memory there is a property of the system that has become permanent. The system has crossed a line in state space that it will not cross again. The state space becomes strictly divided into regions the system may possibly visit again and regions it never will. Of course, an equivalent type of event may happen again, but it will be indexed with a different count. The same juxtaposition of events in the observed system and accounts by the observing system will never again be repeated, if memory faithfully serves.

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But perfect observation and permanent recordings are seldom encountered in real life. Therefore, informational content must be found in the distribution of a system's behavior across the whole state space. A system must be recognized as informed by events whenever this distribution comes to be anything other than uniform, or in relative terms deviating from a known baseline. As to what events caused the information there is no indication yet. That kind of decoding requires another cycle of hypotheses about reliable connections with object systems and experiments that lay odds on the systematic validation of these bets. The impressions that must be worked with have the shape of probability distributions. The impression that an event makes on a system lies in the difference between the total distribution of its behavior and the distribution generated on the hypothesis that the event did not happen.

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A system of observation constitutes a projection of the object system's total behavior onto a space of observations, which may be called the space of observing or observant states. The object system's total state space is not necessarily a well-defined entity. It can only be imagined to lie in some unknown extension of the observing space. How much information a system may have is defined only relative to a particular system of observation. It is often convenient to personify all the various specifications of observational systems and spaces under a single name, the observer. Every bit of information that a system maintains with respect to an observer constrains the system's behavior to half the observed state space it would otherwise have. When designing systems it is preferred that this bit of information reside in a

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A system of observation constitutes a projection of the object system's total behavior onto a space of observations, which may be called the space of observing or observant states. The object system's total state space is not necessarily a well-defined entity. It can only be imagined to lie in some unknown extension of the observing space. How much information a system may have is defined only relative to a particular system of observation. It is often convenient to personify all the various specifications of observational systems and spaces under a single name, the observer. Every bit of information that a system maintains with respect to an observer constrains the system's behavior to half the observed state space it would otherwise have. When designing systems it is preferred that this bit of information reside in a well-defined register, a localized component of anatomical structure in a designed-to-be-known decomposition of the intelligible object system.

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well-defined register, a localized component of anatomical structure in a designed-to-be-known decomposition of the intelligible object system.

However, the kind of direct product decomposition that would make this feasible is not always forthcoming. When investigating a system of unknown design, it cannot be certain that all its information is embodied in localized components. It is not even certain that a given observation system is detecting the level, mode, or site in which the majority of its information is stored. Even when it is found that a system occupies a small selection or a narrow distribution of its possible states and increases its level of informedness with time, this may yield a quantitative measure of its determination and progress but it does not offer a motive, neither a reference to the objects nor a sense of the objectives that may be driving the process.

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In exception to the general rule, some work in AI and cognitive science has reached the verge of applying the homomorphic idea of representation, although in some cases the arrows may be reversed. Notable in this connection is the concept of structure-mapping exploited in (Gentner & Gentner, 1983) and (Prieditis, 1988) and the notion of quasi-morphism introduced in (Holland, et al., 1986). One of the software engineering challenges implicit in this work is to provide the kind of standardized category-theoretic computational support that would be needed to routinely set up and test whole parametric families of such models. An off-the-shelf facility for categorical computing would of course have many other uses in theoretical and applied mathematics.

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=====~~1.2.1.1.~~ Observation and Action=====

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=====Observation and Action=====

It seems clear that observations are a special type of action, and that actions are a special type of observable event. At least, actions are events that may come to be observed, if only in the way that outcomes of eventual effects are recognized to confirm the hypotheses of specific causes. Is every action in some sense an observation? Is every observable event in some sense an observation, a commemoration, an event whose occasion serves to observe something else? If this were so, then the concepts of observation and action would be special cases of each other. Computer scientists will have no trouble accepting the mutual recursion of complex notions, so long as the conceptual instrument as a whole does its job, and so long as the recursion bottoms out somewhere. The mutual definition can find its limit in two ways. It can ground out centrally, with a single category of primitive element that has all the relevant aspects being analyzed, here both perception and action. It can scatter peripherally, resolving into simple elements that distinctively belong to one category or another.

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=====~~1.2.1.2.~~ Observation and Observables=====

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=====Observation and Observables=====

Independently of their distinctness as categories, what is the relation of the observing and the observable as roles played out in the theater of observation? Observation may be the noting of internal or external events, but more than contemplation it requires the possibility of leaving a record. Nothing serves as observation unless notches can be made in a medium that retains the indenture through time. By this analysis, observation is found to be involved in the very same relation that signs have to their objects. The observation is a sign of its observed object, event, or action. In spite of the active character of concrete observation, it still seems convenient in theoretical models (like turing machines) to divide observation across two abstract components: an active, empirical part that arranges apparatus for a complex test and goes looking for what's happening (on unforeseen segments of tape), and a passive, logical part that represents the elementary reception and pure contingency of simply noting without altering what's under one's nose (or read head).

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=====~~1.2.1.3.~~ Observation and Interpretation=====

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=====Observation and Interpretation=====

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The foregoing discussion of observation and observables seems like such a useless exercise in hair-splitting that a forward declaration of its eventual purpose is probably called for at this point. Section 2 will introduce a notation for propositional calculus, and Section 3 will describe a proposal for its differential extension. To anticipate that development a bit schematically, suppose that a symbol "x" stands for a proposition (true-false sentence) or a property (qualitative feature). Then a symbol "dx" will be introduced to stand for a primitive property of "change in x". Differential features like "dx", depending on the circumstances of interpretation, may be interpreted in several ways. Some of these interpretations are fairly simple and intuitive, other ways of assigning them meaning in the subject matter of systems observations are more subtle. In all of these senses the proposition "dx" has properties analogous to assignment statements like "x := x+1" and "x := not x". In spite of the fact

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The foregoing discussion of observation and observables seems like such a useless exercise in hair-splitting that a forward declaration of its eventual purpose is probably called for at this point. Section 2 will introduce a notation for propositional calculus, and Section 3 will describe a proposal for its differential extension. To anticipate that development a bit schematically, suppose that a symbol "x" stands for a proposition (true-false sentence) or a property (qualitative feature). Then a symbol "dx" will be introduced to stand for a primitive property of "change in x". Differential features like "dx", depending on the circumstances of interpretation, may be interpreted in several ways. Some of these interpretations are fairly simple and intuitive, other ways of assigning them meaning in the subject matter of systems observations are more subtle. In all of these senses the proposition "dx" has properties analogous to assignment statements like "x := x+1" and "x := not x". In spite of the fact that its operational interpretation entails difficulties similar to that of assignment statements, I think this notation may provide an alternate way of relating the declarative and procedural semantics of computational state change.

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that its operational interpretation entails difficulties similar to that of assignment statements, I think this notation may provide an alternate way of relating the declarative and procedural semantics of computational state change.

In one of its fuller senses the differential feature "dx" can mean something like this: The system under consideration will next be observed to have a different value for the property "x" than the value it has just been observed to have. As such, "dx" involves a three-place relationship among an observed object, a signified property, and a specified observer. Note that the truth of "dx" depends on the relative behavior of the system and the observer, in a way that cannot be analyzed into absolute properties of either without introducing another observer. If "dx" is interpreted as the expectation of a certain observer, then its realization can be imagined to depend on both the orbit of the system and the sampling scheme or threshold level of the observer. In general, differential features can involve the dynamic behavior of an observed system, decisions about a designated property, and the attention of a specified observer in ways that are irreducibly triadic in their level of complexity.

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If a particular observer is taken as a standard, then discussion reduces to a universe of discourse about various two-place relations, that is, the relations of a system's state to several designated properties. Relative to this frame, a system can be said to have a variety of objective properties. An observer may be taken as a standard for no good reason, but usually a system of observation becomes standardized by exhibiting properties that make it suitable for use as such, like the fabled daily walks of Kant through the streets of Konigsberg by which the people of that city were able to set their watches (Osborne, p. 101). This reduction is similar to the way that a pragmatic discussion of signs may reduce to semantic and even syntactic accounts if the context of usage is sufficiently constant or if a constant interpreter is assumed. Close analogies between observation and interpretation will no doubt continue to arise in the synthesis of physical and intelligent dynamics.

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====~~1.2.2.~~ Symbolic Media====

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====Symbolic Media====

A critical transition in the development of a system is reached when components of state are set aside internally or assimilated from the environment to make relatively irreversible changes, indelible marks to record experiences and note intentions. Where in the dynamics of a system do these signs reside? In what nutations of equilibrium does the system insinuate its libraries of notation, the tokens of passed, pressing, and prospective moments of experience? What parameters are concretely set as memorials to the results of observations performed, the outcomes of actions observed, and the plans of action contemplated to provide the experience of desired effects? What bank accumulates all the words coined and spent on sights and deeds? What mint guarantees the content and determines the form of their first impressions?

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=====~~1.2.2.1.~~ Papyrus, Parchment, Palimpsest=====

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=====Papyrus, Parchment, Palimpsest=====

Starting from the standpoint of systems theory a sizable handicap must be overcome in the quest to figure out: "What's in the brain that ink may character?" and "Where is fancy bred?" (McCulloch, 1965). If localized deposits of historical records and promissory notes are all that can be found, a considerable amount of reconstruction may be necessary to grasp the living reality of experience and purpose that underlies them still. A distinction must be made between the analytic or functional structure of the phase space of a system and the anatomical structure of a hypothetical agent to whom these states are attributed. The separation of a system into environment and organism and the further detection of anatomical structure within the organism depend on a direct product decomposition of the space into relatively independent components whose interactions can be treated secondarily. But the direct product is a comparatively advanced stage of decomposition and not to be expected in every case.

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This point draws the chase back through the briar patch of that earlier complexity theory, the prime decomposition or group complexity theory of finite automata and their associated formal languages or transformation semigroups (Lallement, ch. 4). This more general study requires the use of semi-direct products (Rotman, 1984) and their ultimate extension into wreath products or cascade products, along with the corresponding notions of divisibility, factorization, or decomposition (Barr & Wells, 1990, ch. 11). This theory seems to have reached a baroque stage of development, either too difficult to pursue with vigor, too lacking in applications, or falling short of some essential insight. It looks like another one of those problem areas that will need to be revisited on the way to integrating AI and systems theory.

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=====~~1.2.2.2.~~ Statements, Questions, Commands=====

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=====Statements, Questions, Commands=====

When signs are created that can be placed in reliable association with the results of observations and the onsets of actions, these signs are said to denote or evoke the corresponding events and actions. This is the beginning of ''declarative'', ''imperative'', and ''interrogative'' uses of symbolic expressions.

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If signs and symbols are to receive a place in systems theory it must be possible to construct them from materials available on that site. But the only thing a system has to work with is its own present state. How do states of a system come to serve the role of signs? How can it make sense to say that system regards one of its own states as a sign of something else? How do certain states of a system come to be taken by that system, as evidenced by its interpretive behavior, as signs of something else, some object or objective? A good start toward answering these questions would be made by defining the words used in asking them. In looking at the concepts that remain to be given system-theoretic definitions it appears that all of these questions boil down to one: What character in the dynamics of a system would cause it to be called a sign-using system, one that acts as an interpreter in a non-trivial sense?

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=====~~1.2.2.3.~~ Pragmatic Theory of Signs=====

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=====Pragmatic Theory of Signs=====

The theory of signs that I find most useful was developed over several decades in the last century by C.S. Peirce, the founder of modern American pragmatism. Signs are defined pragmatically, not by any essential substance, but by the role they play within a three-part relationship of signs, interpreting signs, and referent objects. It is a tenet of pragmatism that all thought takes place in signs. Thought is not placed under any preconceived limitation or prior restriction to symbolic domains. It is merely noted that a certain analysis of the processes of perception and reasoning finds them to resolve into formal elements which possess the characters and participate in the relations that a definition will identify as distinctive of signs.

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These remarks should be enough to convey the plan of this work. Progress can be made toward new resettlements of ancient regions where only turmoil has reigned to date. Existing structures can be rehabilitated by continuing to unify the terms licensing AI representations with the terms leasing free space over dynamic manifolds. A large section of habitable space for dynamically intelligent systems could be extended in the following fashion: The images of state and the agents of change that are customary in symbolic AI could be related to the elements and the operators which form familiar planks in the tangent spaces of dynamic systems. The higher order concepts that fill out AI could be connected with the more complex constructions that are accessible from the moving platforms of these tangent spaces.

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====~~1.2.3.~~ Architecture of Inquiry====

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====Architecture of Inquiry====

The outlines of one important landmark can already be seen from this station. It is the architecture of inquiry, in the style traced out by C.S. Peirce and John Dewey on the foundations poured by Aristotle. I envision being able to characterize the simplest drifts of its dynamics in terms of certain differential operators.

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Aristotle long ago pointed out that there can be no genuine science of the purely idiosyncratic subject, no systematic knowledge of the totally isolated event. Science does not have as its domain all experience but only that subset which is indefinitely repeatable. Likewise on the negative branch, concerning the lack of knowledge that occasions a problem, a state that never recurs does not present a problem for a system. This limitation of scientific problems and knowledge to recurrent phenomena yields an important clue. The placement of intelligence and knowledge in analogy with system attributes like momentum and frequency may turn out to be based on deeply common principles.

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=====~~1.2.3.1.~~ Inquiry Driven Systems=====

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=====Inquiry Driven Systems=====

One goal of this work is to develop a formalism adequate to the description of knowledge-oriented inquiry-driven systems in logical and differential terms, to be able to write down and run as simulations qualitative differential equations that describe individual cases of systems with knowledge-directed behavior, systems which exhibit a progress toward a goal of knowledge. A knowledge-oriented system is one which maintains a knowledge base which figures into its behavior in a dual role, both as a guide to action and as the object of a system goal to increase the measure of its usefulness. An inquiry-driven system is one that develops its knowledge base in response to the differences existing between three aspects of state that may be projected or generated from its total state, components which might be called: expectations, observations, and intentions.

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One important use of a system's current knowledge base is to project expectations of what is likely to be actualized in its experience, an image of what state it envisions probable. Another use of a system's personal knowledge base is to preserve intentions during the execution of series of actions, to keep a record of a current goal, a picture of what it would like to find actualized in its experience, an image of what state it envisions desirable. From these uses of images two kinds of differences crop up in the process of inquiry.

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=====~~1.2.3.2.~~ Surprises to Explain=====

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=====Surprises to Explain=====

One of the uses of a knowledge base is to support the generation of expectations. In return, one of the inputs to the operators which edit and update a knowledge base is the set of differences between expected and observed states. An inquiry-driven system requires a function to compare expected states, as represented in the images reflexively generated from present knowledge, and observed states, as represented in the images currently delivered as unquestioned records of actual happenings. In human terms this kind of discrepancy between expectation and observation is experienced as a surprise, and is usually felt as constituting an impulse toward an explanation that can reduce the sense of disparity. The specification of a particular inquiry-driven system would have to detail this relation between states of uncertainty and resultant actions.

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=====~~1.2.3.3.~~ Problems to Resolve=====

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=====Problems to Resolve=====

Since a system's determination of its own goals is a special case of knowledge in general, it is convenient to allocate a place for this kind of information in the knowledge component of an intelligent system. Thus, the intellectual component of a knowledge-oriented system may be allowed to preserve its intentions, the images of currently active goals. Often there is a difference between an actual state, as represented by the image developed in free space by a trusted process of observation, and an active goal, as represented by an image in the same space but cherished within the frame of intention or otherwise distinguished by an attentional affect. This situation represents a problem to be solved by the system through actions that effect changes on the level of its primary dynamics. The system chooses its trajectory in accord with reducing the difference between its active intentions and the observations that record actual conditions.

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====~~1.2.4.~~ Simple Minded Systems====

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====Simple Minded Systems====

Of course, not every total manifold need have a nice factorization. It might be thought to dispense with such spaces immediately, to put them aside as not being reasonable. But it may not be possible to dismiss them quite so easily and summarily. Intelligent systems of this sort may end up being refractory to routine analysis and will have to be regarded as simple minded. That is, they may turn out to be simple in the way that algebraic objects are usually called simple, having no interesting proper factors of the same sort. Suppose there are such simple minded systems, otherwise deserving to be called intelligent but which have no proper factorization into the kind of gross dynamics and subtle dynamics that might correspond to the distinction ordinarily made between somatic and mental behavior. That is, they do not have their activity sorted into separate scenes of action: one for ordinary physical and thermal dynamics, another for information processing dynamics, symbolic operations, knowledge transformations, and so on up the scale. In the event that this idea of simplicity can be found to make sense, it is likely that simple minded systems would be deeply involved in or place extreme bounds on the structures of all intelligent systems.

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The cases of simple minded systems appear to contain at least the following two possibilities. First, a simple minded system may come into being already knowing itself perfectly, in which case all the irony of a Socrates would be lost on it, in terms of bringing it a wit closer to knowledge. The system already knows its whole manifold of possible states, that is, its knowledge component is in some sense complete, containing an answer to every possible dynamic puzzle that might be posed to it. Rather than an overwhelming richness of theory, this is more likely to arise from a structural poverty of the total space and a lack of capacity for the reception of questions that can be posed to it, as opposed to those posed about it. Second, a simple minded system might be born into an initial condition of ignorance, with the potential of reaching states of knowledge within its space, but these states may be discretely distributed in a continuous manifold. This means that states of knowledge could be achieved only by jumping directly to them, without the benefit of an error-controlled feedback process that allows a system to converge gradually upon the goals of knowledge.

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===~~1.3.~~ Telos : Horizons and Further Applications===

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===Telos : Horizons and Further Applications===

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In its etymology, intelligence suggests a capacity that contains its goal (''telos'') within itself. [No, insert correction here.] Of course, it does not initially grasp that for which it reaches, does not always possess its goal, otherwise it would be finished from the start. So it must be that it contains only a knowledge of its goal. This need not be a perfect knowledge even of what defines the goal, leaving room for clarification in that dimension, also. Some thinkers on the question suspect that the capacity for setting goals may answer to another name: wisdom (''sophia''), prudence (''phronesis''), and even elegance (''arete'') are among the candidates often heard. If so, intelligence would have a relationship to this wisdom and sagacity that is analogous to the relationship of logic to ethics and ~~esthetics~~. At least, this is how it appears from the standpoint of one philosophical tradition that recommends itself to me.

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In its etymology, intelligence suggests a capacity that contains its goal (''telos'') within itself. [No, insert correction here.] Of course, it does not initially grasp that for which it reaches, does not always possess its goal, otherwise it would be finished from the start. So it must be that it contains only a knowledge of its goal. This need not be a perfect knowledge even of what defines the goal, leaving room for clarification in that dimension, also. Some thinkers on the question suspect that the capacity for setting goals may answer to another name: wisdom (''sophia''), prudence (''phronesis''), and even elegance (''arete'') are among the candidates often heard. If so, intelligence would have a relationship to this wisdom and sagacity that is analogous to the relationship of logic to ethics and aesthetics. At least, this is how it appears from the standpoint of one philosophical tradition that recommends itself to me.

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====~~1.3.1.~~ Logic, Ethics, ~~Esthetics~~====

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====Logic, Ethics, Aesthetics====

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The philosophy I find myself converging to more often lately is the pragmatism of C.S. Peirce and John Dewey. According to this account, logic, ethics, and ~~esthetics~~ form a concentric series of normative sciences, each a subdiscipline of the next. Logic tells how one ought to conduct one's reasoning in order to achieve the stated goals of reasoning in general. Thus logic is a special application of ethics. Ethics tells how one ought to conduct one's activities in general in order to achieve the good appropriate to each enterprise. What makes the difference between a normative science and a prescriptive dogma is whether this “telling” is based on actual inquiry into the relationship of conduct to result, or not.

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The philosophy I find myself converging to more often lately is the pragmatism of C.S. Peirce and John Dewey. According to this account, logic, ethics, and aesthetics form a concentric series of normative sciences, each a subdiscipline of the next. Logic tells how one ought to conduct one's reasoning in order to achieve the stated goals of reasoning in general. Thus logic is a special application of ethics. Ethics tells how one ought to conduct one's activities in general in order to achieve the good appropriate to each enterprise. What makes the difference between a normative science and a prescriptive dogma is whether this “telling” is based on actual inquiry into the relationship of conduct to result, or not.

In this view, logic and ethics do not set goals, they merely serve them. Of course, logic may examine the consistency of an arbitrary selection of goals in the light of what science tells about the likely repercussions in nature of trying to actualize them all. Logic and ethics may serve the criticism of certain goals by pointing out the deductive implications and probable effects of striving toward them, but it has to be some other science which finds and tells whether these effects are preferred and encouraged or detested and discouraged relative to a particular form of being.

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The science which examines individual goods, species goods, and generic goods from an outside perspective must be an ~~esthetic~~ science. The capacity for inquiry into a subject must depend on the capacity for uncertainty about that subject. ~~Esthetics~~ is capable of inquiry into the nature of the good precisely because it is able to be in question about what is good. Whether conceived as empirical science or as experimental art, it is the job of ~~esthetics~~ to determine what might be good for us. Through the exploration of artistic media we find out what satisfies our own form of being. Through the expeditions of science we discover and further the goals of own species' evolution.

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The science which examines individual goods, species goods, and generic goods from an outside perspective must be an aesthetic science. The capacity for inquiry into a subject must depend on the capacity for uncertainty about that subject. Aesthetics is capable of inquiry into the nature of the good precisely because it is able to be in question about what is good. Whether conceived as empirical science or as experimental art, it is the job of aesthetics to determine what might be good for us. Through the exploration of artistic media we find out what satisfies our own form of being. Through the expeditions of science we discover and further the goals of own species' evolution.

Outriggers to these excursions are given by the comparative study of biological species and the computational study of abstractly specified systems. These provide extra ways to find out what is the sensible goal of an individual system and what is the perceived good for a particular species of creature. It is especially interesting to learn about the relationships that can be represented internally to a system's development between the good of a system and the system's perception, knowledge, intuition, feeling, or whatever sense it may have of its goal. This amounts to asking the questions: What good can a system be able to sense for itself? How can a system discover its own best interests? How can a system achieve, from the evidence of experience, a cognizance, evidenced in behavior, of its own best interests?

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====~~1.3.2.~~ Inquiry and Education====

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====Inquiry and Education====

My joint work with Susan Awbrey speculates on the yield of AI technology for new seasons of inquiry-based approaches to education and research (Awbrey & Awbrey, 1990, '91, '92). A fruitful beginning can be made, we find, by returning to grounds that were carefully prepared by C.S. Peirce and John Dewey, and by asking how best to rework these plots with the implements that the intervening

years have provided. There is currently being pursued a far-ranging diversity of work on the applications of AI to education, through research on problem solving performance (Smith, 1991), learner models and the novice-expert shift (Gentner & Stevens, 1983), the impact of cognitive strategies on instructional design (West, Farmer, & Wolff, 1991), the use of expert systems as teaching tools (Buchanan & Shortliffe, 1984), (Clancey & Shortliffe, 1984), and the development of intelligent tutoring systems (Sleeman & Brown, 1982), (Mandl & Lesgold, 1988). Other perspectives on AI's place in science, society, and the global scene may be sampled in (Wiener, 1950, 1964), (Ryan, 1974), (Simon, 1982), (Gill, 1986), (Winograd & Flores, 1986), and (Graubard, 1988).

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====~~1.3.3.~~ Cognitive Science====

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====Cognitive Science====

Remarkably, seeds of a hybrid character, similar to what is sought in the intersection of AI and systems theory, were planted many years ago by one who explored the farther and nether regions of the human mind. This model blended recognizably cybernetic and cognitive ideas in a scheme that included associative networks for adaptive learning and recursion mechanisms for problem solving. But these ideas lay dormant and untended by their originator for over half a century. Sigmund Freud rightly estimated that this model would always be too simple-minded to help with the complex and subtle exigencies of his chosen practice. But the "Project" he wrote out in 1895 (Freud, 1954) is still more sophisticated, in its underlying computational structure, than many receiving serious study today in AI and cognitive modeling. Again, here is another stage, another window, where old ideas and directions may be worth a new look with the new 'scopes available, if only to provide a basis for informing the speculations that get a theory started.

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The ideas of information processing, AI, cybernetics, and systems theory had more direct interactions, of course, with the development of cognitive science. A share of these mutual influences and crosscurrents may be traced through the texts and references in (Young, 1964, 1978), (de Bono, 1969), (Eccles, 1970), (Anderson & Bower, 1973), (Krantz, et al., 1974), (Johnson-Laird & Wason, 1977), (Lachman, Lachman, & Butterfield, 1979), (Wyer & Carlston, 1979), (Boden, 1980), (Anderson, 1981, '83, '90), (Schank, 1982), (Gentner & Stevens, 1983), (H. Gardner, 1983, 1985), (O'Shea & Eisenstadt, 1984), (Pylyshyn, 1984), (Bakeman & Gottman, 1986), (Collins & Smith, 1988), (Minsky & Papert, 1988), (Posner, 1989), (Vosniadou & Ortony, 1989), (Gottman & Roy, 1990), and (Newell, 1990).

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====~~1.3.4.~~ Philosophy of Science====

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====Philosophy of Science====

Continuing the angle of assault previously taken toward the abandoned mines of intellectual history, there are many other veins and lodes, subsided and shelved, that experts assay too low a grade for current standards of professional work. Yet many of these superseded courses and discredited vaults of theory are worth retooling and remining in the shape of computer models. Computational reenactments of these precept chapters in human thought, not just repetitions but analytic representations, could serve the purpose of school figures, training exercises and stock examples, to be used as instructional paradigm cases.

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A prime example of a project awaiting this kind of salvage operation is the submerged edifice of Carnap's "world building" (1928, 1961), the remains of a mission dedicated to "the rational reconstruction of the concepts of all fields of knowledge on the basis of concepts that refer to the immediately given … the searching out of new definitions for old concepts" (1969, ''v''). The illusory stability of the "immediately given" has never been more notorious than today. But the relevant character to be appreciated in this classical architecture is the degree of harmony and balance, the soundness in support of lofty design that subsists and makes itself evident in the relationship of one level to another. Much that is toxic in our intellectual environment today could be alleviated by a suitably analytic and perceptive movement to recycle, reclaim, and restore the artifacts and habitations of former times.

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==2. Conceptual Framework==

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==Conceptual Framework==

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===~~2.1.~~ Systems Theory and Artificial Intelligence===

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===Systems Theory and Artificial Intelligence===

If the principles of systems theory are taken seriously in their application to AI, and if the tools that have been developed for dynamic systems are cast in with the array of techniques that are used in AI, a host of difficulties almost instantly arises. One obstacle to integrating systems theory and artificial intelligence is the bifurcation of approaches that are severally specialized for qualitative and quantitative realms, the unavoidable differences between boolean-discrete and real-continuous domains. My way of circumventing this obstruction will be to extend the compass of differential geometry and the rule of logic programming to what I see as a locus of natural contact. Continuing the inquiry to naturalize intelligent systems as serious subjects of dynamic systems theory, a whole series of further questions comes up:

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A certain figure of speech, a chiasmus, may be used to get this point across. The universe of discourse, as a system of objective realities, is something that is not yet perfectly described. And yet it can be currently described in the signs and the symbols of a discursive universe. By this is meant a formal language that is built up on terms that are taken to be simple. Yet the simplicity of the chosen terms is not an absolute property but a momentary expedient, a side-effect of their current interpretation.

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===~~2.2.~~ Differential Geometry and Logic Programming===

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===Differential Geometry and Logic Programming===

In this section I make a quick reconnaissance of the border areas between logic and geometry, charting a beeline for selected trouble spots. In the following sections I return to more carefully survey the grounds needed to address these problems and to begin settling this frontier.

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====~~2.2.1.~~ Differences and Difficulties====

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====Differences and Difficulties====

Why have I chosen differential geometry and logic programming to try jamming together? A clue may be picked up in the quotation below. When the foundations of that ingenious duplex, AI and cybernetics, were being poured, one who was present placed these words in a cornerstone of the structure (Ashby, 1956, p. 9).

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A deliberate continuity of method extends from this use of difference in goal-seeking behavior to the baby steps of AI per se, namely, the use of difference-reduction methods in the form of what is variously described as means-ends analysis, goal regression, or general problem solving.

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=====~~2.2.1.1.~~ Distance and Direction=====

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=====Distance and Direction=====

Legend tells us that the primal twins of AI, the strife-born siblings of Goal-Seeking and Hill-Climbing, began to stumble and soon came to grief on certain notorious obstacles. The typical scenario runs as follows.

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Intelligent systems do not get to prescribe the problem spaces that will be thrown their way by nature, society, and the outside world in general. These nominal problems would hardly constitute problems if this were the case. Thus it pays to consider how intelligent systems might evolve to cast ever wider nets of competence in the spaces of problems that they can handle. Striving to adapt the differential strategies of classical cybernetics and of early AI to "soaring" new heights (Newell, 1990), to widening gyres of ever more general problem spaces, there comes a moment when the predicament thickens but the atmosphere of theory and the wings of artifice do not.

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=====~~2.2.1.2.~~ Topology and Metric=====

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=====Topology and Metric=====

Topology is the most unconstrained study of spaces, beginning as it does with spaces that have barely enough hope of geometric structure to deserve the name of spaces (Kelley, 1961). An attention to this discipline inspires caution against taking too lightly the issue of a metric. There is no longer any reason to consider the question of a metric to be a trivial one, something whose presence and character can be taken for granted. For each space that can be contemplated there arises a typical suite of questions about the existence and the uniqueness of a possible metric. Some spaces are not metrizable at all (Munkres, sec. 2-9). Those that are may have a multitude of different metrics defined on them. My own sampling of differential methods in AI, both smooth and chunky style, suggests to me that this multiplicity of possible metrics is the ingredient that conditions one of their chief sticking points, a computational viscosity that consistently sticks in the craw of computers. Unpalatable if not intractable, it will continue to gum up the works, at least until some way is found to dissolve the treacle of complexity that downs our best theories.

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=====~~2.2.1.3.~~ Relevant Measures=====

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=====Relevant Measures=====

Differences between problem states are not always defined. And even when they are, relevant differences are not always defined in the manner that would form the most obvious choice. Relevant differences are differences that make a difference, in the well-known pragmatist phrase, bearing on the problem and the purpose at hand. The qualification of relevance adds information to the abstractly considered problem space. This extra information has import for the selection of a relevant metric, but nothing says it will ever determine a unique metric suited to a given situation. Relevant metrics are generally defined on semantic features of the problem domain, involving pragmatic equivalence classes of objects. Measures of distinction defined on syntactic features, in effect, on the language that is used to discuss the problem domain, are subject to all of the immaterial differences and the accidental collision of expression that acts to compound the computational confusion and distraction.

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When the problem of finding a fitting metric develops the intensity to cross a critical threshold, a strange situation is constellated. The new level of problemhood is noticed as an afterthought but may have a primeval reality about it in its own right, its true nature. The new circle of problem states may circumscribe and underlie the initial focus of attention. Can the problem of finding a suitable metric for the original problem space be tackled by the same means of problem solving that worked on the assumption of a given metric? A reduction of that sort is possible but is hardly ever guaranteed. The problem of picking the best metric for the initial problem space may be as difficult as the problem first encountered. And ultimately there is always the risk of reaching a level of circumspection where the problem space of last resort has no metric definable.

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====~~2.2.2.~~ Logic with a Difference====

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====Logic with a Difference====

In view of the importance of differential ideas in systems theory and against the background of difficulties just surveyed, I have thought it worthwhile to carefully pursue this quest: to extend the concepts of difference and due measure to spaces that lack the obvious amenities and expedients. The limits of rational descriptive capacity for any conceivable sets of states have their ultimate horizon in logic. This is what must be resorted to when only qualitative characterizations of a problem space are initially available. Therefore I am led to ask what will be a guiding question throughout this work: What is the proper form of a differential calculus for logic?

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===~~2.3.~~ Differential Calculus of Propositions===

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===Differential Calculus of Propositions===

There are two different analogies to keep straight in the following discussion. First is the comparison of boolean vs. real types with regard to functions and vectors. These types provide mathematical representation for the qualitative vs. quantitative constituencies, respectively. Second is the three-part analogy within the qualitative realm. It relates logical propositions with mathematical functions and sets of vectors, both functions and vectors being of boolean type.

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====~~2.3.1.~~ Propositions and Differences====

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====Propositions and Differences====

As a first step, I have taken the problem of propositional calculus modeling and viewed it from the standpoint of differential geometry. In this I exploit an analogy between propositional calculus and the calculus on differential manifolds. In the qualitative arena propositions may be viewed as boolean functions. They are associated with areas or arbitrary regions of a Venn diagram, or subsets of an n-dimensional cube. Logical interpretations, in the technical sense of boolean-valued substitutions in propositional expressions, may be viewed as boolean vectors. They correspond to single cells of a Venn diagram, or points of an n-cube. Put altogether, these linkages form a three part analogy between conceptual objects in logic and the two mathematical domains of functions and sets. In its pivotal location, critical function, and isosceles construction this analogy suggests itself as the pons asinorum of the subject I can see developing. But I can't tell till I've crossed it.

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====~~2.3.2.~~ Three Part Analogy====

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====Three Part Analogy====

For future use it is convenient to label the various elements of the three-part analogy under discussion.

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=====~~2.3.2.1.~~ Functional Representation=====

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=====Functional Representation=====

Functional representation is the link that converts logical propositions into boolean functions. Its terminus is an important way station for mediating the kinds of computational realizations I hope eventually to reach. This larger endeavor is the project of declarative functional programming. It has the goal of giving logical objects a fully operational meaning in software, implementing logical concepts in a functional programming style without sacrificing any of their properly declarative nature. I have reason to hope this can be a fruitful quest, in part from the reports of more seasoned travelers along these lines, e.g. (Henderson, 1980), (Peyton Jones, 1987), (Field & Harrison, 1988), (Huet, 1990), (Turner, 1990).

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This knowledge would consist of axioms, inferential procedures, and a running accumulation of theorems. A developmental programming system of this sort would permit designers to anticipate many features of contemplated programs before running the risk of risking to run them. One vital requirement of the ideal system must be provisioned in the most primitive elements of its construction. The ideal system plus knowledge-base plus intelligence needs to be developmental in the added sense of a developing mentality. Undistracted by all the positive features that an ideal system must embody, a great absence must also be arranged by its designers. To the extent foreseeable there must be no foreclosure of interpretive freedom. The intended programming language, the sans critical koine of the utopian realm, must place as little possible prior value on the primitive tokens that fund its form of expression. An early implementation of a knowledge-based system for program development, using a refinement tree to search a space of correct programs, is described in (Barstow, 1979).

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=====~~2.3.2.2.~~ Characteristic Relation=====

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=====Characteristic Relation=====

Characteristic relation denotes the two-way link that relates boolean functions with subsets of their boolean universes, whether pictured as Venn diagram regions or ''n''-cube subsets does not matter. Indicative conversion describes the traffic or exchange on this link between the two termini. Given a set ''A'', the function ''f''''A'' which has the value 1 on ''A'' and 0 off ''A'' is commonly called the ''characteristic function'' or the ''indicator function'' of ''A''. Since every boolean function ''f ''determines a unique set ''S'' = ''S''''f'' of which it is the indicator function ''f'' = ''f''''S'' , this forms a convertible relationship between boolean functions and sets of boolean vectors. This fact is also described as an isomorphism between the function space (''U'' → '''B''') and the power set ''P''(''U'') = 2''U'' of the universe ''U''. The associated set ''S''''f'' is often called the ''support'' of the function ''f''. Alternatively, it may serve as a helpful mnemonic and a useful handle on this edge of the analogy to call ''S''''f'' the ''characteristic region'', ''indicated set'', or simply the ''indication'' of the function ''f'', and to say that the function ''characterizes'' or ''indicates'' the set where its value is positive (that is, greater than 0, and therefore equal to 1 in '''B''').

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=====~~2.3.2.3.~~ Indicative Conversion=====

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=====Indicative Conversion=====

The term indicative conversion and the associated usages are especially apt in light of the ordinary linguistic relationship between declarative sentences and verb forms in the indicative mood, which "represent the denoted act or state as an objective fact" (Webster's). It is not at all accidental that a fundamental capacity needed to support declarative programming is the pragmatic facilitation of this semantic relation, the ready conversion between propositions as indicator functions and properties in extension over indicated sets. The computational organism that would function declaratively must embody an interior environment with plenty of catalysts for the quick conversion of symbolically expressed functional specifications into images of their solution sets or sets of models.

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====~~2.3.3.~~ Pragmatic Roles====

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====Pragmatic Roles====

The part of the analogy that carries propositions into functions combines with the characteristic relation between functions and sets to generate a multitude of different ways to describe essentially the same conceptual objects. From an information-theoretic point of view "essentially the same" means that the objects in comparison are equivalent pieces of information, parameterized or coded by the same number of bits and falling under isomorphic types. When assigning characters to individual examples of these entities, I think it helps to avoid drawing too fine a distinction between the logical, functional, and set-theoretic roles that have just been put in correspondence. Thus, I avoid usages that rigidify the pragmatic dimensions of variation within the columns below:

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Though it may be advisable not to reify the practical distinctions among these roles, this is not the same thing as failing to see them or denying their use. Obviously, these differences may vary in relative importance with the purpose at hand or context of use. However, the mere fact that a distinction can generally be made is not a sufficient argument that it has any useful bearing on a particular purpose.

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=====~~2.3.3.1.~~ Flexible Roles and Suitable Models=====

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=====Flexible Roles and Suitable Models=====

When giving names and habitations to things by the use of letters and types, a certain flexibility may be allowed in the roles assigned by interpretation. For example, in the form "''p'' : ''U'' → '''B'''", the name "''p''" may be taken to denote a proposition or a function, indifferently, and the type ''U'' may be associated with a set of interpretations or a set of boolean vectors, correspondingly, whichever makes sense in a given context of use. One dimension that does matter is drawn through these three beads: propositions, interpretations, and values. On the alternate line it is produced by the distinctions among collections, individuals, and values.

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The interpretations that render a proposition true, that is, the substitutions for which the proposition evaluates to true, are said to satisfy the proposition and to be its models. With a doubly modulated sense that is too apt to be purely accidental, the model set is the "content" of the proposition's formal expression (Eulenberg, 1986). In functional terms the models of a proposition ''p'' are the pre-images of truth under the function ''p''. Collectively, they form the set of vectors in ''p''–1(1). In another usage the set of models is called the ''fiber'' of truth, in other words, the equivalence class [1]''p'' of the value 1 under the mapping ''p''.

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=====~~2.3.3.2.~~ Functional Pragmatism=====

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=====Functional Pragmatism=====

The project of functional programming itself fits within a broader philosophical mission, the pragmatism of C.S. Peirce and John Dewey, which seeks to clarify abstract concepts and occult properties by translating them into operational terms, see (Peirce, Collected Papers) and (Dewey, 1986). These thinkers had clear understandings of the relation between information and control, giving early accounts of inquiry processes and problem-solving, intelligence and goal-seeking that would sound quite familiar to cyberneticians and systems theorists. Similar ideas are reflected in current AI work, especially by proponents of means-ends analysis and difference reduction methods (Newell, 1990), (Winston, ch. 5).

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Of all the complex systems that attract human interest, the human mind's own doings, knowing or not, must eventually form a trajectory that ensnares itself in questions and wonderings: Where will it be off to next? What is it apt to do next? How often will it recur to the various things it does? The mind's orbit traced in these questions has a compelling power in its own right to generate wonder.

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====~~2.3.4.~~ Abstraction, Behavior, Consequence====

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====Abstraction, Behavior, Consequence====

There are many good reasons to preserve the logical features and constraints attaching to computational objects, i.e. programs and data structures. Chief among these reasons are: axiomatic abstraction, behavioral coordination, and consequential definition.

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=====~~2.3.4.1.~~ Axiomatic Abstraction=====

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=====Axiomatic Abstraction=====

The capacity for abstraction would permit an expert system for dynamic simulation to rise above the immediate flux of the process simulated. Eventually, this could enable the software intelligence to adduce, reason about, and test hypotheses about generic properties of the system under study. Even short of this autonomy, the resources of abstract representation could at least provide a medium for transmuting embedded simulations into axioms and theories. For the systems prospector such an interface, even slightly reflective, can heighten the chances of panning some nugget of theory and lifting some glimmer of insight from the running stream of simulations.

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=====~~2.3.4.2.~~ Behavioral Coordination=====

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=====Behavioral Coordination=====

The guidelines of pragmatism are remarkably suited as regulative principles for synthesizing AI and systems theory, where it is required to clarify the occult property of intelligence in terms of dynamic activity and behavior. This involves realizing abstract faculties, like momentum and intelligence, as hypotheses about the organization of trajectories through manifolds of observable features. In these post-revolutionary times, cognitively and chaotically speaking, it is probably not necessary to be reminded that this effort contains no prior claim of reductionism. The pragmatic maxim can no more predetermine the mind to be explained by simple reflexes than it can constrain nature to operate by linear dynamics. If these reductions are approximately true of particular situations, then they have to be discovered on site and proven to fit, not imposed with eyes closed.

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=====~~2.3.4.3.~~ Consequential Definition=====

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=====Consequential Definition=====

The ability to deduce consequences of specified/acquired features and generic/imposed constraints would support the ultimate prospects toward unification of several stylistic trends in programming. Among these are the employment of class hierarchies and inheritance schemes in frame-system and semantic network knowledge bases (Winston, ch. 8), object-oriented programming methodologies (Shriver & Wegner, 1987), and constraint based programming (Van Hentenryck, 1989). The capacity for deduction includes as a special case the ability to check logical consistency of declarations. This has applications to compilation type-checking (Peyton Jones, 1987) and deductive data-base consistency (Minker, 1988).

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====~~2.3.5.~~ Refrain====

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====Refrain====

The analogy between propositional calculus and differential geometry is extended as far as possible by continuing to cast propositions and interpretations in roles similar to those exercised by real-valued functions and real-coordinate vectors in the quantitative world. In a number of reaches tentative trials of the analogy will render fit correspondences. Beyond these points it is critically important to examine those stretches where the analogy breaks, and there to consider the actual temperament and proper treatment of the qualitative situation in its own right.

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A text that has been useful to me in relating classical and modern treatments of differential geometry is (Spivak, 1979). The standard for logic programming via general resolution theorem proving was set by (Chang & Lee, 1973). A more recent reference is (Lloyd, 1987), which concentrates on Prolog type programming in the Horn clause subset of logic. My own incursions through predicate calculus theorem proving and my attempts to size up the computational complexity invested there have led me to the following opinions.

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===~~2.4.~~ Logic Programming===

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===Logic Programming===

Militating against the charge of declarative programmers to achieve their goals through logic, a surprising amount of computational resistance seems to reside at the level of purely sentential or propositional operations. In investigating this situation I have come to believe that progress in logic programming will be severely impeded unless these factors of computational complexity at the level of propositional calculus are addressed and either resolved or alleviated.

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At my current state of understanding I can propose nothing more complicated than to work toward a position of increased knowledge about the practical logistics of this problem domain. A reasonable approach is to explore the terrain at this simplest level, using the advantages afforded by a propositional calculus interpreter and relevant utilities in software. A similar strategy of starting from propositional logic and working up in stages to predicate logic is exploited by (Maier & Warren, 1988), in this case building a Prolog interpreter by successive refinement.

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====~~2.4.1.~~ Differential Aspects====

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====Differential Aspects====

The fact that a difference calculus can be developed for boolean functions is well-known (Kohavi, sec. 8-4,), (Fujiwara, 1985) and was probably familiar to Boole, who was a master of difference equations before he turned to logic. And of course there is the strange but true story of how the Turin machines of the 1840's prefigured the Turing machines of the 1940's (Menabrea, p. 225-297). At the very outset of general-purpose, mechanized computing we find that the motive power driving the Analytical Engine of Babbage, the kernel of an idea behind all his wheels, was exactly his notion that difference operations, suitably trained, can serve as universal joints for any conceivable computation (Morrison & Morrison, 1961), (Melzak, ch. 4).

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====~~2.4.2.~~ Algebraic Aspects====

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====Algebraic Aspects====

Finally, there is a body of mathematical work that investigates algebraic and differential geometry over finite fields. This usually takes place at such high levels of abstraction that the field of two elements is just another special case. In this work the principal focus is on the field operations of sum (<math>+</math>) and product ( <math>\cdot</math> ), which correspond to the logical operations of exclusive disjunction (xor, neq) and conjunction (and), respectively. The stress laid on these special operations creates a covert bias in the algebraic field. Unfortunately for the purposes of logic, the totality of boolean operations is given short shrift on the scaffold affecting this algebraic slant. For example, there are sixteen operations just at the level of binary connectives, not to mention the exploding population of ''k''-ary operations, all of which deserve in some sense to be treated as equal citizens of the logical realm.

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Moreover, from an algebraic perspective the dyadic or boolean case exhibits several features peculiar to itself. Binary addition (<math>+</math>) and subtraction (<math>-</math>) amount to the same operation, making each element its own additive inverse. This circumstance in turn exacts a constant vigilance to avert the verbal confusion between algebraic negatives and logical negations. The property of being invertible under products ( <math>\cdot</math> ) is neither a majority nor a typical possession, since only the element 1 has a multiplicative inverse, namely itself. On account of these facts the strange case of the two element field is often set aside, or set down as a "degenerate" situation in algebraic studies. Obviously, in turning to take it up from a differential standpoint, any domain that confounds "plus" and "minus" and "not equal to" is going to play havoc with our automatic intuitions about difference operators, linear approximations, inequalities and thresholds, and many other critical topics.

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===~~2.5.~~ Differential Geometry===

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===Differential Geometry===

One of the difficulties I've had finding guidance toward the proper form of a differential calculus for logic has been the variety of ways that the classical subjects of real analysis and differential geometry have been generalized. As a first cut, two broad philosophies may be discerned, epitomized by their treatment of the differential d''f'' of a function f : ''X'' → '''R'''. Everyone begins with the idea that d''f'' ought to be a locally linear approximation d''f''''u''(''v'') or d''f''(''u'', ''v'') to the difference function D''f''''u''(''v'') = D''f''(''u'', ''v'') = ''f''(''u'' + ''v'') – ''f''(''u''). In this conception it is understood that "local" means in the vicinity of the point ''u'' and that "linear" is meant with respect to the variable ''v''.

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====~~2.5.1.~~ Local Stress and Linear Trend====

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====Local Stress and Linear Trend====

But one school of thought stresses the local aspect, to the extent of seeking constructions that can be meaningful on global scales in spite of coordinate systems that make sense solely on local scales, being allowed to vary from point to point, for example, (Arnold, 1989). The other trend of thinking accents the linear feature, looking at linear maps in the light of their character as representations or homomorphisms (Loomis & Sternberg, 1968). Extenuations of this line of thinking go to the point of casting linear functions under the headings of the vastly more general morphisms and abstract arrows of category theory (Manes & Arbib, 1986), (MacLane, 1971).

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=====~~2.5.1.1.~~ Analytic View=====

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=====Analytic View=====

The first group, more analytic, strives to get intrinsic definitions of everything, defining tangent vectors primarily as equivalence classes of curves through points of phase space. This posture is conditioned to the spare frame of physical theory and is constrained by the ready equation of physics with ante-metaphysics. In short they regard physics as a practical study that is prior to any a priori. Physics should exert itself to save the phenomena and forget the rest. The dynamic manifold is the realm of phenomena, the locus of all knowable reality and the focus of all actual knowledge. Beyond this, even attributes like velocity and momentum are epiphenomenal, derivative scores attached to a system's dynamic point from measurements made at other points.

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This incurs an empire of further systems of ranking and outranking, teams and leagues and legions of commissioners, all to compare and umpire these ratings. When these circumspect systems are not sufficiently circumscribed to converge on a fixed point or a limiting universal system, it seems as though chaos has broken out. The faith of this sect that the world is a fair game for observation and intelligence seems dissipated by divergences of this sort. It wrecks their hope of order in phenomena, dooms what they deem a fit domain, a single rule of order that commands the manifold to appear as it does. To share the universe with several realities, to countenance a real diversity? It ruins the very idea they most favor of a cosmos, one that favors them.

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=====~~2.5.1.2.~~ Algebraic View=====

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=====Algebraic View=====

The second group, more algebraic, accepts the comforts of an embedding vector space with a less severe attitude, one that belays and belies the species of anxiety that worries the other group. They do not show the same phenomenal anguish about the uncertain multiplicity or empty void of outer spaces. Given this trust in something outside of phenomena, they permit themselves on principle the luxury of relating differential concepts to operators with linear and derivation properties. This tendency, ranging from pious optimism to animistic hedonism in its mathematical persuasions, demands less agnosticism about the reality of exterior constructs. Its pragmatic hope allows room for the imagination of supervening prospects, without demanding that these promontory contexts be uniquely placed or set in concrete.

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=====~~2.5.1.3.~~ Compromise=====

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=====Compromise=====

In attempting to negotiate between these two philosophies, I have arrived at the following compromise. On the one hand, the circumstance that provides a natural context for a manifold of observable action does not automatically exclude all possibility of other contexts being equally natural. On the other hand, it may happen that a surface is so bent in cusps and knots, or otherwise so intrinsically formed, that it places mathematical constraints on the class of spaces it can possibly inhabit.

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Thus a manifold can embody information that bears on the notion of a larger reality. By dint of this interpretation the form of the manifold becomes the symbol of its implicated unity. But what I think I can fathom seems patent enough, that the chances of these two alternatives, plurality and singularity, together make a bet that is a toss up and open to test with each new shape of manifold encountered. It is likely that the outcome, if at all decidable, falls in accord with no general law but is subject to proof on a case by case basis.

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====~~2.5.2.~~ Prospects for a Differential Logic====

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====Prospects for a Differential Logic====

Pragmatically speaking, the ''proper'' form of a differential logic is likely to be regulated by the purposes to which it is intended to be put, or determined by the uses to which it is actually, eventually, and suitably put. With my current level of uncertainty about what will eventually work out, I have to be guided by my general intention of using this logic to describe the dynamics of inquiry and intelligence in systematic terms. For this purpose it seems only that many different types of ''fiber bundles'' or systems of ''spaces at points'' will have to be contemplated.

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Although the limited framework of propositional calculus seems to rule out this higher level of generality, the exigencies of computation on symbolic expressions have the effect of bringing in this level of arbitration by another route. Even though we use the same alphabet for the joint basis of coordinates and differentials at each point of the manifold, one of our intended applications is to the states of interpreting systems, and there is nothing a priori to determine such a program to interpret these symbols in the same way at every moment. Thus, the arbitrariness of local reference frames that concerns us in physical dynamics, that makes the arbitrage or negotiation of transition maps between charts (qua markets) such a profitable enterprise, raises its head again in computational dynamics as a relativity of interpretation to the actual state of a running interpretive program.

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===~~2.6.~~ Reprise===

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===Reprise===

In summing up this sample of literature bearing on my present aims, there is much to suggest a deep relationship between the topics of systems, differentials, logic, and computing, especially when considered in the accidental but undeniable stream of historical events. I have not come across any strand of inquiry that plainly, explicitly, and completely weaves differential geometry and propositional logic in a computational context. But I hope to see one day a scintilla of a program that can weld them together in a logically declarative, functionally dynamic platform for intelligent computing.

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==3. Instrumental Focus==

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==Instrumental Focus==

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===~~3.1.~~ Propositional Calculus===

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===Propositional Calculus===

A symbolic calculus is needed to assist our reasoning and computation in the realm of propositions. With an eye toward efficiency of computing and ease of human use, while preserving both functional and declarative properties of propositions, I have implemented an interpreter and assorted utilities for one such calculus. The original form of this particular calculus goes back to the logician C.S. Peirce, who is my personal favorite candidate for the grand-uncle of AI. Among other things, Peirce discovered the logical importance of NAND/NNOR operators (CP 4.12 ff, 4.264 f), (NE 4, ch. 5), inspired early ideas about logic machines (Peirce, 1883), is credited with "the first known effort to apply Boolean algebra to the design of switching circuits" (M. Gardner, p. 116 n), and even speculated on the nature of abstract interpreters and other "Quasi-Minds" (Peirce, CP 4.536, 4.550 ff).

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All of these issues that occupied Peirce would be encountered again later in the 20th century when computer scientists, linguists, communication engineers, media theorists, and others would be forced to deal with them in their daily practice and would perforce discover many workable answers. These are the topics that have come to be recognized as the reality of information and uncertainty, the physicality of symbol systems, the independent dimension of syntax, the complexity of semantics and evaluation, the pragmatic metes and bounds of interactive communication and interpretive control. All in all, as acutely discovered in AI systems engineering, these factors sum up to the general resistance of matter to being impressed with our minds.

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====~~3.1.1.~~ Peirce's Existential Graphs====

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====Peirce's Existential Graphs====

Peirce devised a graphical notation for predicate calculus, or first order logic, that he called the system of "Existential Graphs" (EG). In its emphasis on relations and its graphic depiction of their logic, EG anticipated many features of present-day semantic networks and conceptual graphs. Not only does it remain logically more exact than most of these later formulations, but EG had transformation rules that rendered it a literal calculus, with a manifest power for inferring latent facts. An explicit use of Peirce's EG for knowledge base representation appears in (Sowa, 1984). A software package that uses EG to teach basic logic is documented in (Ketner, 1990). The calculus presented below is related in its form and interpretation to the propositional part of Peirce's EG. A similar calculus, but favoring an alternate interpretation, was developed in (Spencer-Brown, 1969).

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=====~~3.1.1.1.~~ Blank and Bound Connectives=====

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=====Blank and Bound Connectives=====

Given an alphabet A = {'''a'''1, …, '''a'''''n''} and a universe ''U'' = áAñ, we write expressions for the propositions ''p'' : ''U'' → '''B''' upon the following basis. The '''a'''''i'' : ''U'' → '''B''' are interpreted as coordinate functions. For each natural number ''k'' we have two ''k''-ary operations, called the ''blank'' or ''unmarked'' connective and the ''bound'' or ''marked'' connective.

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|}

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=====~~3.1.1.2.~~ Partitions : Genus and Species=====

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=====Partitions : Genus and Species=====

Especially useful is the facility this notation provides for expressing partition constraints, or relations of mutual exclusion and exhaustion among logical features. For example,

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says that the genus ''g'' is partitioned into the three species ''s''1, ''s''2, ''s''3. Its venn diagram looks like a pie chart. This style of expression is also useful in representing the behavior of devices, for example: finite state machines, which must occupy exactly one state at a time; and Turing machines, whose tape head must engage just one tape cell at a time.

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=====~~3.1.1.3.~~ Vacuous Connectives and Constant Values=====

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=====Vacuous Connectives and Constant Values=====

As a consistent downward extension, the nullary (or 0-ary) connectives can be identified with logical constants. That is, blank expressions " " are taken for the value ''true'' ("silence assents"), and empty bounds "( )" are taken for the value ''false''. By composing operations, negation and binary conjunction are enough in themselves to obtain all the other boolean functions, but the use of these ''k''-ary connectives lends itself to a flexible and powerful representation as graph-theoretical data-structures in the computer.

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====~~3.1.2.~~ Implementation Details====

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====Implementation Details====

The interpreter that has been implemented for EG employs advanced data-structures for the reprsentation of both lexical terms and logical expressions.

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Mathematically, all this verbiage is just a way of talking about two topics: (1) functions and relations from structured objects to sets of features, and (2) equivalence relations (for example, orbits under symmetry group actions) on these structured objects. But the visual metaphors seem to assist thought, most of the time, and are in any case a part of the popular iconography.

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=====~~3.1.2.1.~~ Painted Cacti=====

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=====Painted Cacti=====

Viewing a propositional expression in EG as a "cactus", the bound connectives ( , , , ) constitute its "lobes" (edges or lines) and the positive literals ai are tantamount to "colors" (paints or tints) on its "points" (vertices or nodes). One of the chief tasks of processing logical expressions is their systematic clarification. This involves transforming arbitrary expressions into logically equivalent expressions whose latent meaning is manifest, their "canonical" or "normal" forms. The normalization process implemented for EG, in the graphical language just given, takes an arbitrary tinted cactus and turns it into a special sort of painted cactus.

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=====~~3.1.2.2.~~ Concept and Purpose=====

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=====Concept and Purpose=====

What good is this? What conceivable purpose is there for these inductive and deductive capacities, that enable the personal computer to learn formal languages and to turn propositional calculi into painted cacti? By developing these abilities for inductive learning and accurate inference, aided by a facility for integrating their alternate "takes" on the world, I hope that AI software will gain a new savvy, one that helps it be both friendly to people and faithful to truth, both politic and correct. To do this demands a form of artificial intelligence that can do both, without the kinds of trade-off that make it a travesty to both.

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====~~3.1.3.~~ Applications====

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====Applications====

The current implementation of this calculus is efficient enough to have played a meaningful part in realistically complex investigations, both practical and theoretical. For example, it has been used in qualitative research to represent observational protocols of event sequences as propositional data bases. It has also been used to analyze the behavior of finite state machines and space-time limited Turing machines, exploiting a coding that is similar to but more succinct than the one used in Cook's theorem (on the NP-completeness of propositional calculus satisfiability). See (Garey & Johnson, 1979) and (Wilf, 1986).

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===~~3.2.~~ Differential Extensions of Propositional Calculi===

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===Differential Extensions of Propositional Calculi===

In order to define a differential extension of a propositional universe of discourse ''U'', the alphabet A of ''U''’s defining features must be extended to include a set of symbols for differential features, or elementary "changes" in the universe of discourse. Intuitively, these symbols may be construed as denoting primitive features of change, or propositions about how things or points in ''U'' change with respect to the features noted in the original alphabet A. Hence, let dA = {da1, …, da''n''} and d''U'' = ádAñ = áda1, …, da''n''ñ. As before, we may express d''U'' concretely as a product of distinct factors:

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'''Note.''' This bibliography belongs to a larger paper still in progress.

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'''Added Note.''' I lost the file with the bibliography in the process of switching between computers a few years back, and was left with only the hard copies. I'm still “getting around to” retyping the thing. —JA

Jon Awbrey

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