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[RRE]The Practical Logic of Computer Work

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The Practical Logic of Computer Work

Philip E. Agre Department of Communication University of California, San Diego La Jolla, California 92093-0503 USA

phone: +1 (619) 534-6328 fax: +1 (619) 534-7315 net: pagre@ucsd.edu home: http://communication.ucsd.edu/pagre/

Revised version of a paper presented at the Conference of the Society for Social Studies of Science, Tucson, October 1997. 3400 words.

Italics are in asterisks, book titles in underscores.

In his reading of John Dee's sixteenth-century commentary on Euclid, Knoespel (1987) describes what he calls the "narrative matter of mathematics": the discursive work by which an artifact such as a drawing on paper is exhibited as embodying a mathematical structure such as a Euclidean triangle. This process, which is familiar enough to modern technologists as the work of mathematical formalization, consists of a transformation of language into something quite different from language, namely mathematics, and its challenge is to circumvent or erase those aspects of language which are incompatible with the claim of mathematics to represent the world in a transparent, unmediated, ahistorical way. Knoespel explains the larger significance of the point like this:

... the reification of geometry in architecture and technology has enormous implications for language. Once geometry becomes manifest in artifacts, these artifacts retain an authority an authority radically different from that accessible to natural language. By virtue of their being manifested as physical objects they acquire what appears as an autonomy utterly separated from language. The apparent separation of both architecture and technology from language has great significance, for it works to repress the linguistic framework that has allowed it to come into being. (1987: 42)

Brian Smith (1996) makes a similar point when he speaks of "inscription errors": inscribing one's discourse into an artifact and then turning around and "discovering" it there. An example might be the front door of an office building. Beyond its functions in controlling entry, the door also serves a narrative function: someone happening upon it can recognize it as a door, and it is designed in part to be recognizable as a door and to be narratable in those terms. The point is familiar from Schutz (1967 [1927?]) and Garfinkel (1984 [1967]): the social reality of the square resides in a reflexive relationship between designer and user, and the success of that relationship lies precisely in the achieved unremarkability of that relationship and the possibility of routinely locating the "squareness" in the artifact itself.

This observation about geometry exercises and architecture may have little import in itself, but its significance is more evident in the case of another large category of artifacts, namely computers. The design of a computer begins with formalization -- an intensive and highly skilled type of work on language. A computer is, in an important sense, a machine that we build so that we can talk about it in a certain way; it is successful if its operation can be narrated using a certain vocabulary. As the machine is set to running, we too easily overlook the complicated reflexive relationship between the designer and user upon which the narratability of the machine's operation depends. The corpus of language that was transformed in producing the machine is necessarily, like any discourse or text, embedded in an institutional and historical context, and the machine itself must therefore be understood as being, in some sense, a text.

For practitioners of a computational research tradition such as artificial intelligence, however, the textuality of computers is a distraction at best, to the extent that it is even comprehensible as an analysis of technical work. Like John Dee, the practitioners of AI with whom I worked for several years, and among whom I was trained, regarded formalization very differently, as a means precisely of freeing their work from what they regarded as the unruliness and imprecision of vernacular language. As a result, I want to argue, the field has been left with no effective way of recognizing the systemic difficulties that have arisen as the unfinished business of history has irrupted in the middle of an intendedly ahistorical technical practice. My purpose in diagnosing this situation is not to discredit mathematics, technology, or even AI. Instead, I want to envision what it would be like to have a critical technical practice: a technical practice within which such reflection upon language and history, ideas and institutions, is part and parcel of technical work itself.

My point of departure is what AI people themselves have taken to calling Good Old-Fashioned AI -- the complex of metaphors and techniques from which the great majority of the AI literature until recently has begun. Even those projects that do not fully embrace this standard theory are usually well-understood as incrementally relaxing its assumptions in an attempt to resolve its seeming inherent tensions. (Documentation of all of these characterizations of AI, and details on many of the analyses of them, can be found in Agre (1998).)

This standard theory encompasses, among other things, particular theories of representation, perception, and action. The theory of representation holds that knowledge consists in a model of the world, formalized (when it is formalized) in terms of the Platonic analysis of meaning in the tradition of Frege and Tarski. Perception is a kind of inverse optics: building a mental model of the world by working backwards from sense-impressions, inferring what in the world might have produced them. Action, finally, is conducted through the construction and execution of plans, understood roughly as computer programs. This theory is founded in several oppositions, and I want to focus on the specific way in which these oppositions figure in the history of the field. They are as follows:

mind versus world, plans versus behavior, planning versus improvisation, the mind versus the body, and abstract ideals versus concrete things.

Throughout AI, one encounters a certain pattern in the handling of these oppositions. I will refer to this pattern as "dissociation". Dissociation has two moments: an overt distinction between the two terms and a covert conflation between them. As the language of overt and covert may suggest, dissociation is not consciously avowed. It must be sought in the internal tensions of texts and in the dynamics of technical work. Dissociation occurs in many ways, none of which is fixed or stable. To the contrary, much of the history of AI can be understood as attempts to comprehend and manage the signs of trouble that originate in dissociation and that AI practitioners can recognize within the conceptual system of the field. One purpose of a critical technical practice is to recognize such troubles more fully and to diagnose them more deeply.

Allow me to review the workings of dissociation in the five cases I enumerated above as they become manifest in particular texts and projects. The distinction between mind and world has obviously been construed in many ways by different philosophical systems, but in AI it has been operationalized in terms of a sharp qualitative distinction between the inside of the machine and the world outside. The difficulty is that it is enormously difficult to maintain accurate knowledge of a world that is understood to be so distinct and distant. As a result, it is a common idealization in the field to suppose that one's world model -- the mental structure that constitutes one's knowledge of the world -- is complete in every relevant respect and stays up-to-date automatically. Indeed, it is common for AI people in particular and computer people in general to employ the same words to name both the representations in a machine and the things that those representations represent. This conflation between mind and world is facilitated by the use of "domains" such a geometry and chess which are readily understood in mathematical terms. The erasure of language that Knoespel discovered in Dee is here employed on a grand scale to facilitate the conflation, not just of mathematics and the physical world, but mental representation and the world.

The point is not that AI practitioners do this deliberately. Quite the contrary, their work is well understood as an attempt not to do it. Having acquired this conflation between mind and world through the discursive practices they inherited from earlier intellectual projects, AI people continually find the conflation reinforced through the practical logic of their research. If I may risk a mathematical metaphor, the dissociative relationship between mind and world is an attractor: although unsatisfactory as a theory of human life, it is also difficult to escape by incremental modifications because the covert conflation between mind and world solves, even if pathologically, a problem for which no less pathological solution is known. The mathematical metaphor here is misleading, though, in that the terrain is a function of consciousness: which moves in the space of research proposals seem comprehensible and well-motivated is itself a function of the critical awareness through which the symptoms of technical trouble are perceived and interpreted. So long as the larger pattern remains ungrasped, the attractor retains its grip on the research.

The second dissociation is between mental activity and perception. AI originated as part of the cognitivist movement whose other was behaviorism. In combatting the behaviorist hegemony, the task of cognitivism was to present itself as scientific by the same standard as behaviorism did, while also opposing itself as squarely as possible to behaviorism on a substantive level. As Karl Lashley observed in an influential 1951 symposium paper, the behaviorist theory of action was stimulus-chaining: the idea that each action in a serially ordered sequence arises as a response to, among other things, the effects of the previous action in the sequence. Lashley urged on psychophysical grounds, however, that serially ordered action, for example in language, must be understood in terms of an internal organizing principle, which he understood as a holistic mental process.

In his effort to oppose himself to the behaviorist story, Lashley equivocates on the role of perception in organizing behavior. A fair-minded reader will reconstruct his argument as advocating a two-factor theory of behavior: continual perceptual inputs into the ongoing mental process, and continual outputs from that process in the form of spoken phonemes and other actions drawn from a language-like repertoire. But in fact Lashley provides little substantive account of the role of perception, emphasizing instead that serial behavior is planned and organized in advance and performed in scripted wholes. Dissociation here takes a different form: rather than uniting bodily through systematic ambiguity, the two terms are brought together in two separate theories which are then superimposed. One of these theories, the mental scripting of action, is given the more overt billing because of its simplicity and its polemical role, while the other theory, the one that provides a clear place for perception, floats indistinctly in the background.

A similar pattern can be observed in the case of the third dissocation, which I have termed planning versus improvisation. Remarkably, neither of these terms is prominent in the locus classicus of AI's discourse of planning, Miller, Galanter, and Pribram's "Plans and the Structure of Behavior" (1960). Miller, Galanter, and Pribram argue that behavior has a structure because it results from the execution of a mental entity that has that same structure, namely a Plan. Although it is openly speculative and presents no computer programs or laboratory experiments of its own, "Plans and the Structure of Behavior" served as a rhetoric for the emerging cognitivist movement. Its most distinctive rhetorical device was a systematic conflation between behavior and Plans. To this day, it is customary in AI research to refer to any structure in behavior as a plan, not as a hypothesis but as a simple matter of terminology.

Miller, Galanter, and Pribram inherited the dissociation that was already present in Lashley, and they took it a step further. Close reading demonstrates the presence of two distinct theories, one of which speaks of discrete, prescripted Plans and another of which speaks of a single Plan that unfolds incrementally through time. The former theory, the one that describes a repertoire of discrete Plans, is the official theory: it is the theory that explains the structure of behavior. Yet it is an unsatisfactory theory in many ways, and so it is overlaid with the unofficial theory of a single Plan that can be constructed in a more improvisational manner as an ongoing response to perception. The authors return to the matter again by offering a theory of Plans based on then-current ideas of servocontrol. This third theory is laid atop the earlier two with little overt sense of their incompatibility; in any case, it is the equivocal relation between the first two theories that set the agenda for subsequent research. Most of this research focused exclusively on the construction of discrete plans, thus obviating the need for a theory of improvisation. It is computationally very difficult to construct a Plan that will work reliably in a world of any great complexity, however, and so the second of Miller, Galanter, and Pribram's theories was rediscovered with great energy by AI researchers such as myself in the 1980's. One term of the dissociation, having been suppressed, returned. Yet when it did so, its internal relationship to the dominant theory was hardly recognized, and research in that era saw several attempts to resynthesize the two approaches, reproducing in the architecture of cognitive models the conflation that occurred more covertly in Miller, Galanter, and Pribram's text.

The fourth dissociation is between planning and execution, and here again the covert conflation between the two terms takes a different form. It can be seen in the first computer implementation of the plans-and-execution theory, the 1972 STRIPS model of Fikes and Nilsson, which automatically constructed plans and then executed them by directing the movements of a robot. These authors' dilemma is that it is hard to predict in enough detail how the world will unfold as the robot executes its plans, and yet it is so expensive to construct a new plan that it seems necessary to execute as much of each plan as possible. At one point they suggest simply executing the first step of each new plan and then constructing a new plan from scratch, but they reject the idea as impractical. The dissociation between planning and execution manifests itself here in a seeming attempt by the two dissociated categories to become reunited, or at least to become intertwined with one another. The impracticality of the dissociation is revealed through the process of system-building. It is here, in the design and construction of systems, that AI learns most of its lessons, and not in formal experimentation with the systems once they are running. If I seem to anthropomorphize the desire of planning and execution to reverse their dissociation, this is only to give voice to a common experience of system designers: the very tangible set of pressures that seem to push the design process in one direction or another.

The final dissociation is between abstraction and concrete reality in the tradition of Fregean semantics. Historically, theories of semantics have drifted between two poles: psychologism is the theory that meanings are mental entities, and Platonism is the theory that meanings reside in a timeless, extensionless realm of ideals. What is most striking is how little difference this distinction has made in practice. The mind, as it has been constructed through the system of dissociations that I have already described, resembles the realm of Platonic ideals in many ways. The use of mathematical domains helps resolve this tension as well, inasmuch as mathematical entities resemble Platonic ideals and are now routinely employed to formalize Platonic theories of the semantics of both language and logic.

The problem arises whenever the mind must be understood as participating in the historical, causal world. The problem manifests itself in many ways, but it can already be clearly grasped in Frege's own theory. Frege's theory of sense, that is, the cognitive content of a representation, conflates incompatible goals. The sense of a representation is supposed to be a Platonic ideal, independent of place and time, and yet it is also held to incorporate specific objects such as a car or a person. The problem is particularly acute in the case of indexical representations, whose sense is plainly dependent on the concrete historical circumstances of their use. I can use words like "here" and "now" perfectly well without knowing where I am or what time it is, and yet the sense of my utterance involves those specific places and times. The trouble arises because Frege's theory of sense, and a whole subsequent tradition of semantic theories, attempts to capture the content of thoughts and utterances without reference to the embodied activities and relationships with which they are used. As the realm of Platonic abstractions is conflated with the realm of concrete activity, the semantic formalism takes on a covertly material character and the theory of mind takes on a covertly disembodied character.

These, then, are five dissociations that have organized the practical logic of research within the leading tradition of artificial intelligence. The interaction among these dissociations defines a complex and variegated field whose contradictory pressures must be reconciled somehow in each project in its own unique way. In most cases I have chosen particular projects to illustrate the practical logic of dissociation. Other projects may well be discovered to manage the tensions in a different way. Yet few AI projects, if any, have reflected a systematic understanding of them, much less any resolution.

Where do these dissociations originate? Do all conceptual distinctions lead to dissociations when they are embodied in technical work? When AI work is framed in terms of these dissociations, it becomes obvious that AI is part of the history of Western thought. It has inherited certain discourses from that history about matters such as mind and world, and it has inscribed those discourses in computing machinery. The whole point of this kind of technical model-building is conceptual clarification and empirical evaluation, and yet AI has failed either to clarify or to evaluate the concepts it has inherited. Quite the contrary, by attempting to transcend the historicity of its inherited language, it has blunted its own awareness of the internal tensions that this language contains. These tensions have gone underground, emerging through substantive assumptions, linguistic ambiguities, theoretical equivocations, technical impasses, and ontological confusions.

Yet the project of artificial intelligence has not been a failure. The discipline of technical implementation, considered in another way, really has done its job. The dissociations of AI have refused to go away, and in the practical work of model-building they have ceaselessly reasserted themselves. As AI practitioners have tried to manage all of the many cross-cutting tensions in their work, they have made the generative principle of those tensions ever more visible to those with the critical means to look. This is the motivation for my advocacy of a critical technical practice, and the basis of my positive hopes for it. Bad theories of human existence, we might conjecture, do not simply fail to correspond to the data; they are in fact impracticable, in that nobody and nothing could live in the ways they describe. As AI people learn to expand their understanding of the ways in which a system can "work" or "not work", AI can perhaps become a means of listening to reality and of learning from it. The key to this transition is a reversal of the ideological function of technology that Knoespel described at the outset. The texts that we inscribe in computing machinery are texts in the fullest sense. In particular, they always have greater depths than we are aware of, and they always encode sophisticated strategies for managing the contradictory experience of the world that we have inherited from the past. Computers and formalization do not eliminate all this; quite the contrary, they make it more visible.

And what exactly has AI made visible in this fashion? Running through all of the dissociations I mentioned a recurring theme, a kind of transcendentalism that attempts to hold something apart from and above material reality. The transcendentalist philosophy of AI is that the outside world is, to use the military language that AI took over during the 1980's, uncertain, unpredictable, and changing. In each case, the world is a negative, chaotic principle and the mind is a positive, ordering principles. What we might tentatively conclude, in the irreducibly intuitive and practical way in which such conclusions might possibly be drawn from technical work, is that it doesn't work that way. If not for the positive ordering principles inherent in the world itself, life would be impossible. Man, in this traditional sense, cannot live by imposing order, but only by participating in it. Only from this standpoint amidst the order of the world does the world itself become visible only then does the question usefully arise of how best to live in the world, and how best to change it.

//* Bibliography

Philip E. Agre, Computation and Human Experience, Cambridge: Cambridge University Press, 1997.

Harold Garfinkel, Studies in Ethnomethodology, Polity Press, 1984. Originally published in 1967.

Kenneth J. Knoespel, The narrative matter of mathematics: John Dee's Preface to the Elements of Euclid of Megara (1570), Philological Quarterly 66(1), 1987, pages 27-46.

Karl S. Lashley, The problem of serial order in behavior, in Lloyd A. Jeffress, ed, Cerebral Mechanisms in Behavior: The Hixon Symposium, New York: Wiley, 1951.

George A. Miller, Eugene Galanter, and Karl H. Pribram, Plans and the Structure of Behavior, New York: Henry Holt and Company, 1960.

Alfred Schutz, The Phenomenology of the Social World, translated by George Walsh and Frederick Lehnert, Evanston: Northwestern University Press, 1967.

Brian C. Smith, On the Origin of Objects, Cambridge: MIT Press, 1996.

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