Rescuing CAD from rationalism

Rescuing CAD from rationalism Richard Coyne and Adrian Snodgrass, Faculty of Architecture, University of Sydney, Sydney, NSW, 2006, Australia Artificial

Intelligence

valuable

insights

to be grounded Descartes.

(AI)

within

There

the tacit

acceptance

Heidegger

asserts

involvement

holds

into design

sway

in some

quarters

and the use of computers

the ‘traditional’epistemology

are problems the primacy

ontology.

however,

In contrast,

of the inconspicuous

of an understanding

as the start

world

of being.

attributable of total

as the basis

of understanding.

workings

of metaphor

are posited

as the bases of an alternative

have terms

at our

disposal

of its usefulness

Keywords:

design,

for for

CAD,

Computation

is just

understanding

design,

to of

view

A Heideggerian

computation of design.

of

the ontology

displaces understanding

AI seems

and ontology

with AI in design,

of this limited

as providing in design.

In this article

one of many

the

metaphors

and is open for

evaluation

we in

design.

artificial

intelligence,

rationalism,

metaphor,

hermeneutics

A

rtifical Intelligence (AI) is thought to provide valuable insights into design and how computers can be used in design. AI is a practical concern, though its claims to practicality are under serious challenge, particularly within the area of design. Design practitioners, industry and funding agencies are becoming less impressed by the distant promises of AI and require immediate and demonstrably useful working systems. Another aspect of the practicability of AI is the claim that it is teaching us something about human thought and intelligence, specifically about design. A test for the usefulness of these insights is in education. How does an AI-based understanding of design assist in the teaching or practice of design? How enlightening are the conclusions that design is a search process within a space defined by changing operators and ill-specified goals; the interaction of multiple agents; the modification of existing cases retrieved from memory; or the settling of a network on a plateau of activations? On the other hand there is much that AI is silent about: the design team, social interaction, and context, to say nothing of meaning and value. Until recently computers were a mystery to practitioners and educators. It was possible to trade on this mystique in sustaining the importance of AI.

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0 1993 Butterworth-Heinemann

Ltd

The powers and limitations of computers and computation are now much more widely understood. The climate is one of scepticism. * Before presenting an account of AI’s difficulties it is necessary to state what is meant by AI in this context. In general terms AI in design can be seen as the application of symbol systems, formal logic, mathematics and formal geometry to an understanding of design. Beyond this the definition is highly volatile. There is a pretence at understanding ‘human reasoning’ though any claims that researchers are ‘reproducing how designers do things in their minds’ is generally regarded as naive. It is more usual to suggest that design exists as a phenomenon and that this phenomenon is being modelled, much as it is thought that we can model the motion of billiard balls or the weather on a computer. This account is certaintly problematic. The usual criteria adopted by science for testing the plausibility of a particular model do not appear to hold for human activity’. The least controversial claim of AI in design is that it involves using symbol processing for creating better tools for designers. AI researchers seem to range freely over explanations of what they are doing, encompassing computational design, modelling design, modelling design knowledge, modelling the minds of designers, simulating design, automating design and providing tools for designers. As will be considered subsequently, the philosophical tradition out of which this research arises leads researchers to see the definitions of AI as relatively unimportant. They see the whole ethos of applied computation within the domain of design as undeniably appropriate. They think that at worst AI is harmless. at best it shows enormous promise.

1 Snodgrass A and Coyne R D ‘Models. metaphors and the hermeneufics of designing’ Design /ssues. Vof 9 NO 1 (1992) 5674

But AI in design does not appear to be producing useful results. Claims to its success are usually couched in terms of future promise. The defence often made is that at least we have made a start in understanding the complex phenomenon of design. Perhaps through diligent research and more powerful hardware we will discover that the relatively simple concepts of AI scale-up to produce something very rich and complex. Beneath this reductive notion is the belief in rationalism amplified through a tacit acceptance of materialism. It is a pervasive belief within this age of science and information technology that underlying all our understanding of the universe is the notion of atomic units of matter or energy which interact via mechanism. This parallels the computational idea of atoms of information able to be processed through computation. So physics and mathematics are at the heart of our understanding. The world around us is a scaled-up version of a complex of interactions. It is thought that we easily lose sight of the simple because of the richness and randomness evident in the whole that it produces.

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Of course there are few people who are complete materialists. A modified version of this rationalism and materialism is to reserve the opinion that there may be something more: that the whole may perhaps produce something different in kind than the sum of the parts; or that there are entities other than matter and mechanism. These entities are thought to belong to the realm of the mysterious. These entities are the subject of speculation, but they are considered not to belong within the realm of academic deliberations or scholarly papers. It is considered that the only respectable way to conduct scholarly research in some quarters is at least to assume that physics and mathematics are at the heart of all our understanding. This appears to be the starting point of AI research. These views are entrenched within AI research, and they appear to have achieved the status of ‘common sense.’ There is therefore a restless disregard for critics of AI’+!. They are seen as mischievous, jealous or aggrieved. Is there a serious alternative to rationalism? The alternative to rationalism is to undertake a conceptual switch and to recognize that experience is at the heart of all understanding. This is not the abstract ‘sense experience’ of empiricism but the shared experience of a community of interlocutors actively engaged in acting and reflecting. It does not take much argument to impress the primacy of experience. Scientific observation and theory formulation are particular kinds of experience. The idea of atoms of matter and energy, mechanism and information are derivative, theoretical and abstract products of these activities. Acknowledging the primacy of experience is more radical than it may sound at first. The experience of the failure of AI to impinge on design practice precedes the importance of the theories on which AI is based. For example, there is no point in holding on to a belief that language (or design) is logic based when everything in our experience tells us that it is not. A concern with theory and practice is replaced by experience and reflection on that experience. This is a recognition that scholarship is not founded upon the development of theories against which we measure our experience. 2

Dreyfus Ii L What Compufers Can? Do: The Limits of Arlificial Intelligence, Harper and Row. New York (1972) Searle .I R ‘Minds. brains and programs Sehav Brain Sci Vol 3 (1960) 417-424 4 Wlnograd T and Flows F Understanding Computers and Cognition: A New Foundation for Design Addison Wesley. Reading, MA (1966)

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Why do certain scholars tenaciously hold on to their theories in the light of contradictory experience? The answer put forward here lies in the legacy of Descartes that still holds sway within whole areas of scholarship, including design theory. So the philosophical basis of AI in design rests within the ontological and epistemological tradition established by Descartes, particularly as developed by positivist philosophers such as Comte, Russell and the early Wittgenstein. Certain aspects of Cartesianism are severely under challenge from within philosophy, social theory

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and language theory at present 5.6. This challenge goes well beyond the ‘mind-body dualism’ that occupies some philosophers of AI, but impinges on the very idea of philosophical foundations, the primacy of theory, the notions of objectivity and subjectivity and the definition of reason. In this article, the philosophy of Heidegger’ (particularly as recently and lucidly explicated by Dreyfus’) will be brought to service in overturning the sway of the Cartesian legacy on design research. Our argument also uses many of the themes developed by Winograd and Flares’.

1 The Cartesian legacy There are four major aspects of Descartes’ philosophy to which we wish to draw attention here. First, there is Descartes’ assertion of the independence of reason. It is possible to detach ourselves from our personal concerns and prejudices to arrive at truth. This is accomplished through a method. The method followed by Descartes, which he adapted from geometric analysis and algebra, was to begin with that which was beyond doubt, to break problems into their simplest constituents, to proceed from the simple to the complex through reasoning, and to test the results through enumeration. According to Descartes every accomplishment of man’s knowledge is linked by long chains of reasoning of the kind evident in geometrical proofs. All knowledge is accessible in this way - ‘. there can be nothing so distant that one does not reach it eventually, or so hidden that one cannot discover itY. This capacity for pure reason is ‘natural’ but requires a trained mind that is prepared to divorce itself from dogma and prejudice. ‘I hope that those who use only their pure natural reason will be better judges of my opinions than those who believe only in the books of the ancients”‘.

Oxford (1960) 6 ~ernsteln R J Beyond Obj.%fivism and Relativism. Basil Blackwell, Oxford (1963) Heidegger M Being and Time (transl J Macquarria and E Robinson) Basil Blackwell, Oxford (1962) 6 Dreyfus H L Eeing-in-fheWorld: A Commentary On Heidegger$ Being and Time. Division 1, MIT Press. Cambndge. MA (1991) 9 Descartes R Discourse on Method and Ihe Medilalions (trawl F E Sutclilfa) Penguin. Harmondsworth. Middlesex (1966) p 41 10 Ibid.. p 91

7

Descartes’ philosophy reaches us in modified form through the mediation of the empiricists such as Kant and Hume who asserted the importance of sense experience. However, the belief in this independence of reason or ‘transparency of mind’ pervades the thinking of both rationalism and empiricism. It is the basis of the ‘objectivity’ of modern science. It is also the basis of the epistemological tradition that sees knowledge as the accumulation of propositions, each founded on prior propositions to form a single ‘edifice.’ There is clearly a tendency to accept that the beliefs we hold are self evident, common sense or able to be derived through inference. According to Heidegger we gravitate towards this position. We do not want to see our beliefs as idiosyncratic, culturally situated or worse, arbitrary. We readily forget the influences that brought us to a particular belief. Appropriation of beliefs seems to be equated with clarity of vision. If we

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change our minds it is because we now see things more clearly. Others may help us to make the logical jumps to speed the process, but we have the ability to get there eventually through our innate capacity for independent reason. The second feature of Descartes’ philosophy is the separation of thinking subject from the world of objects. Descartes begins with primacy of the experience of the self, even the self independent of body. Since the time of Descartes this independence of the subject been held to be self evident, though problematic. The dissolution of subject-object divide is part of the philosophical project of Heidegger.

the the the has the

The third feature is the priority of the individual subject. Descartes wrote ‘I am’ rather than ‘man is.’ Descartes’ Discourse on Method is a solitary journey of discovery. The individual subject contrasts with Plato’s view. According to Plato the mind of those within the social order committed to reason, the law makers, had access to the Intellect, which was a universal, supra-individual and shared ‘organ’ providing access to the realm of the Ideas”-‘3. For Plato and Aristotle it was the social order that set the stage for how we understand the person. Of course, Descartes’ individualism is only possible when we assert the primacy of some capacity for independent reason. Having begun with the individual person, other considerations are thought to follow. We begin with our own intentional states and then derive shared public meanings. The moods, behaviours and opinions of the group are derived from the interactions of a collection of individual moods, behaviours and opinions. The presence and capacities of other minds are understood in terms of our experience of our own mind.

11 Plato The Republic of Plato (Iransl F MacDonald Cornlord) Oxford Unwersity Press, London (1941) 12 Plato Timasus and Crilias (trans~ D gee) Penguin. London (1965) 13 Snodgrass A Architeclure. Time and Efernifv: Studies in fhe Sleflar and Ten&al SymboBsm of Tradilional Buildings, Volume 1. Adilya. New Delhi (1990) 14 Heidegger M Being and Time. op. cil pp 122-134 16 Ibid.. pp 123 16 Dreyfus H L Being-in-theWorld, op. cit.. p 108

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Fourth, according to the Cartesian ontology (the philosophy of existence or being) we can intellectualize an understanding of the world and our place in it in a hierarchical manner”. According to the Cartesian method we begin with the simple, the incontrovertible, and build up a complex picture. The picture may not have originally been derived following this method, but we use the method to reconstruct the picture. The Cartesian ontology begins with the notion that things exist ‘out there’ and we are observing them. Objects exist as comprised of energy and matter. These impinge on the senses. Combinations of atoms result in objects with properties and behaviours. Then follow functions and meanings. So the world and its objects are understood in terms of complex combinations of features. According to Heidegger the Cartesian ontology tells us that ‘substances become accessible in their “attributes.” ‘I6 Every substance has some distinctive property from which its essence can be ‘read off.’

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Various ontological themes have arisen from this simple understanding. According to the prominent cognitivist view the mind contains symbolic representations of features. I6 For the materialist the story extends to the constitution of the mind. Feelings, emotions and consciousness become epiphenomena of extremely large and complex material systems”.“. According to materialism the Cartesian ontology can be mapped onto computer programs. The atoms of existence become information units in a data base. Programs and knowledge bases are the means of making inferences to higher-level representations. Meanings are derived by inference. For many this ontology, and its variants, are self evident, beyond dispute and of little intellectual interest. The Cartesian ontology pervades scientifically-oriented societies. It is regarded as hardly worthy of study. But on reflection this ontology can be seen to be bedevilling in various ways’“. As will be considered subsequently problems in AI can be seen as direct manifestations of the problems of the Cartesian ontology. What of the success of science, the greatest accomplishment arising from the Cartesian ontology? Science can be seen as a particularly fragile and rarefied enterprise in that every attempt is made to suspend the involvement of experience and to turn it into observation2”. Even in science the rules of the subject-object game are under revision, and are possibly at their limits, particularly in the realm of quantum physics, and certainly in the area of understanding human behaviour.

17 Minsky M The Sociefy 01 Mind Heineman, London (1987) 18 Boden M The Philosophy of Artilicial ln~elligence Oxford University Press. Oxford (1990) 19 Heidegger M Being and Time, op. cit.. p 124 Maturana H ‘Reality: the search for objectivity or the quest for a compelling argument’ Irish J ol Psycho! Vol 9 No 1 (19SS) 2%S2.29-29 21 Dreyius H L Being-in-theWorld, op.cit., p 225

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Rather than employ science and the Cartesian ontology to provide a framework for understanding experience, Heidegger turns the question around. Why is it that for our culture certain aspects of human experience (particularly the pursuit of science) convince us that we are dealing with an objective reality, immutable principles, the essential and irrefutable argument? For Heidegger the quest for objective reality, particularly according fundamental status of the idea of the thinking subject attending to the world of objects, represents a kind of ‘falling.‘2’ It is a legitimate state to be in, but represents a transition from the ‘primordial’ to the derivative.

2 Heidegger’s

on tology

One major contribution of the current shift in philosophical thinking: attributable to the slow permeation of ideas put forward by Husserl, pragmatists such as Dewey and reformed positivists such as Wittgenstein: can be seen as a rejection of the legacy of Descartes in which esoteric theories exist independently of human experience. The phenomenology

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of Husserl advocated a return to the way things appear. More recently, and from a scientific background, Lakoff and Johnson’“.‘” have provided a vivid account of the primacy of experience, particularly bodily-based experience, in how we understand all aspects of human thought, including language, the claims of science to objectivity, and the abstract constructs of logic and mathematics. Accounts of the primacy of experience in biological science are also provided by Maturana”‘. Counter to the Cartesian legacy is the ontology of Heidegger’. The tenets of Heidegger’s philosophy contradict and directly challenge the Cartesian ontology with its starting point in the divide between subject and object. Furthermore, taking up Heidegger’s themes, Gadamer” establishes the impossibility of an ‘unprejudiced mind.’ In total the picture is one of a set of new priorities that effectively dispenses with the tenets of rationalism: the undifferentiated world of involvement precedes the divided world of subject and object. In countering the Cartesian ontology Heidegger posits an ‘experientalist’ account. Of course, experience does not exist in a vacuum. Living and relating experience will always be a matter of consensus, discussion and persuasion. The reflection feeds back into the experience. The test for a set of philosophical propositions will always be: is this how it appears? Philosophical propositions will always be subject to change in the light of new reflections.

22

Lakoll G Women. Firs. and Dangerous Things: Whaf Cafegories Reveal abouf the Mind Unwers~ty 01 Chicago Press. Chicago (1967) 23 Lakofi G and Johnson M Metaphors We Live Sy University of Chicago Press. Chicago (1960) 24 Gadamer H-G Trufh and M&hod Sheed Ward, London (1975) 25 ~reyfus H L Being-m-lheWorld, op. cit., p 124 26 Wlnograd T and Flares F Understanding Computers and Cognrtion. op. at 27 Ibid.. p 93 26 Ibid.. p 66 29 Heidegger M Being and Time. op. cit.. pp 96-99

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It is possible to gain an initial understanding of Heidegger’s ontology in terms of levels of experience (or more accurately, ‘modes of being”“). (A helpful introduction to Heidegger’s philosophy in the context of AI is provided by Winograd and Flares”.) According to Heidegger our primary experience of the world is undifferentiated. We are absorbed. As we engage in our activities things are simply nvtrilrtble. We are unaware of the world: ‘. . we often experience ourselves as active yet are not aware of what we are trying to do.“” Our thoughts are not directed to some end. There are no goals: ‘. . at times one is actually surprised when the task is accomplished, as when one’s thoughts are interrupted by one’s arrival at the office.“’ How is it that we are able to cope in this way? Clearly, this coping is ‘shaped by a vast amount of previous dealings’2x - experience. The well known example of this experience of the nvuiluble is our use of an item of equipment such as a hammer’“. The most primordial experience we have of a hammer is through its ‘readiness-to-hand.’ Readinessto-hand is not grasped theoretically or by looking at and contemplating the hammer, but by unselfconsciously using it. In this experience the

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hammer the

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identification Ibid.. p 79 Dreyfus H L Bsing-m-fheWorld. op. cit.. p 43 32 Heldegger M Being and Time. op. al.. p 190 Gadamer H-G Trulh and Method. op. cit.. p 350

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31

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107

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108

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H L Seing-in-fheWorld op. cit.. p 134

is that

traditional

philosophy

34 Dreyfus

the

this

and

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on the

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Dreyfus

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ontology

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to a rarefied

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scientific science to

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a role

ontology

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with

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primacy

of the

to say about available.

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skillfully.

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are

no sense

without

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the individual? Our

is our

of having being

Studies

basic

other

Vol

We can

experience

starting

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point

a role

in life,

roles.

We

14 No 2 April

in but

do not

1993

make sense of ourselves other than in a society”. In the same way that our use of an item of equipment such as a hammer proceeds from a background of understanding of an ‘equipmental whole’ (the materials of the workshop for example), all our activities proceed within a background of shared cultural practices: a ‘societal whole.’ The sphere of the individual presupposes ‘the disclosure of one shared world.‘sh The background practices through which we cope with the world are socially and experientially acquired. At radical variance with the Cartesian assertion of the primacy of ‘transparency of mind’ is the fact that ‘the ultimate “ground” of intelligibility is simply shared practices.‘s’ Shared practice ‘takes the place of the individual subject as the source of significance this blocks the Cartesian claim that meaning is grounded in the activity of an individual subject and thereby undermines the traditional claim that meaning is grounded in an absolute source.‘s’ Of course this ontology is prone to serious misunderstandings. An extremely superficial reading of Heidegger’s ontology may tempt us to see it as a commentary on human psychology and perception. So in our experience of the available we find that as our attention is directed to one thing we find that we cannot attend to the other: perhaps a limitation on our information processing capabilities. As we become familiar with our environment we attend to it less consciously. According to this misreading certainly still rests in the notion of a world out there, an objective reality. It assumes we are primarily beings endowed with consciousness and intelligence able to engage in perceptions and actions. One of the points of Heidegger’s ontology is that availableness precedes scientific explanations of human experience, to which psychological explanations belong’“. To avoid this psychological trap, and for other reasons, Heidegger is at pains to meticulously define terms and to create special terms that do not carry with them the overload of the Cartesian ontology.

35

36 37

38 39

Ibid., Ibid.. Ibid.. Ibid.. Ibid..

p 148 p 142 p 157 p 158 pp W-87

Lest we think that the Heideggerian ontology simply presents an alternative way of looking at things that can be comfortably meshed with a Cartesian world view, it is worth reflecting on the incommensurability of the styles of philosophical discourse provided by each. (The different responses to ‘continental philosophy’ by the English-speaking intellectual community is a study in itself. The prevailing response is still one of suspicion.) In beginning with the phenomenon, and acknowledging our involvement in the world as primary, we embark on a new adventure of intellectual discovery. When taken seriously Heidegger’s ontology changes the rules of the intellectual game. It subjugates theory. It changes

Rescuing CAD from rationalism

109

the

framework

within

philosophers

for

If we take

Heidegger’s

a ‘mind

game’

through

the

and

as underpinning

human

who

support

have

been

usurped,

Once

the tenets

exist

essential

that

allows

tional founded

and

of

theme

from

was not explicitly the rich

to us once

with

a plurality

contexts.

There

developed

computawell-

‘plural

realism’

a metaphoric

status.

by Heidegger,

accounts

rnetrrphor

of human

of rationalism

is

position

as privileged,

have

of

purpose.

of Heidegger’s

and varied

the tenets

for

to some

rationalism,

status

all accounts,

of

mathematics

in a privileged

Within

the position

modes

the question

are

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Richards I A The Phdosophy ol Rhetoric Oxford University Press. London (1936) 41 Rlcoeur P The Rule of Metaphor (tram1 R Czerny with K McLaughlm and J Costello) Routledge and Kegan Paul. London (1977)

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14 No 2 April

1993

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44

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CAD

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of logical D M and Travis L E ‘The computational metaphor and artifiaal mtelligence: a rellective examination of a theoretical falsework A/ Mag Spring (1991) 64-79 43 Ortony A (ed.) Mefaphor and Thought Cambrldge Univers~ty Press. CambrIdge (1979) Sch6n D D~splacemenf ol Concsp!s Tavistock, London (1963)

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and

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42 west

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111

the rationalistic imperative of logic and causality they argue that the metaphor only has relevance within a particular context of experience. Other metaphors of thought cited by Schon include the metaphor of scales. The use of this metaphor is in evidence where the consequences of certain actions have to be weighed against each other. We talk of the weight of evidence being in favour of a particular decision. Ethical problems are frequently discussed in these terms: ‘On balance it seemed appropriate to favour the needs of those dependent on public transport.’ According to Schon there are also metaphors of social processes. Here there are different parts to the self: ‘I told myself .,’ ‘my conscience told me .,’ ’ my will and reason are at odds.’ Under this metaphor structure designing becomes a conversation between ‘internal advocates’ of different kinds of rationality (or advocates making use of different metaphor structures): ‘It seemed logical to keep the structure simple, but I wanted the design to be a bold statement.’ There are also metaphors of mechanism and dynamism in which the mind is a machine. According to some process models from the design methods schoo? decisions appear as switches or taps. Schon suggests that arguments about freedom and determinism are also essentially appealing to the mechanistic metaphor structure. It is important to note that these metaphor structures do not belong merely to the realm of folk psychology but feature as the driving metaphors of those disciplines that make mind their special object of study: psychology and cognitive science. These metaphors cannot necessarily be reconciled into one master theory of thinking. Of course precise typologies of metaphor are also highly elusive. Metaphor structures overlap and subsume one another in complex ways. The way in which metaphor operates has been studied in relation to language at some length’“. We should note that metaphor use is not simply a case of looking at a phenomenon, such as design, and seeing what it has in common with computation, a set of scales, or a chain of atoms before deciding which metaphor to use. Design, or anything else, cannot be understood except metaphorically. We bring such metaphors with us to our understanding of situations. Metaphors prestructure our experiences and are in turn changed by those experiences, a process best described in terms of the metaphors of play and dialogue’.

45

Heath Architecture (1984)

112

T Msfhod ,n Wiley. Chnchestet

So in our exploration of phenomena, and in our acting in the world, we traverse freely through metaphor structures such as those described above. The questions are now: are all metaphor structures equally useful?

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1993

There are certain benefits built into the metaphor structures we commonly employ. For example, according to Schon, for the tools metaphor applied to thought a person manipulates aspects of herself (such as attitudes or beliefs) while retaining a sense of distance and controlJh. However, some metaphors may be identified as disabling within particular contexts. Schon gives the example of a metaphor structure that has pervaded thinking in the area of planning and social policyJ7. This is the metaphor structure that identifies social ills and endorses a re-integration from social breakdown: a metaphor of fragrnerztrrtiorl. He contrasts this with what he regards as the more enabling metaphor structure of social planning as pnrticipntion. Whereas the prevalence of certain ‘disabling’ metaphors is clearly an important issue, it is within the nature of the use of metaphor that the process remains in a state of flux. Any metaphor structure that becomes fixed or achieves exalted status can be disabling. This can be the case where science-based metaphors are applied to design. Models, of the kind put forward in science, are essentially frozen or fixed metaphors’. While exploring the behaviour of light as waves we exclude from consideration its behaviour as particles (or paint or quicksilver). Operating within the theoretical realm of the ‘occurrent’ it is part of the game of science to operate as if a particular metaphor structure were fixed, such as the basis of thought in computation. By inheriting the plausibility of science, models of design, as promoted within AI, can effectively overwhelm other metaphor structures and thereby inhibit effective discussion and action. The metaphor can take over and govern our design actions and discourse to the exclusion of what is made available by other helpful metaphor structures.

4 Evaluating the computational metaphor

46

Sch6n D Disp/acemenr ol Concepb. op. cil.. p 127 Schbn D ‘Generative metaphor: a perspective on problem-setling in scaal policy’ in Ortony. (Ed) Metaphor and Thought Cambridge University Press. Cambridge (1979) pp 254-283

47

We are now in a position to evaluate the computational metaphor of design. Any evaluation should be extensive, discursive and involve the practical application of the metaphor. On the positive side of the computational metaphor it can be said that the communities of researchers who operate within this paradigm have kept design alive as a subject of study and reflection, arguably with greater enthusiasm, coherence and persistence than any other group. Thanks to an enthusiasm (even though it may be misguided) for logic they regard it as important to ‘get things right.’ A challenge to their position is taken seriously rather than merely scoffed at or ignored. Thanks to the central place held by computer technology there is a common bond, a common language and a common base of skills amongst researchers. This engenders a certain camaraderie that ensures communication. AI researchers are indefatigable communicaters, thanks to a plethora of conferences, journals and the electronic

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mail service. Any community in which there is communication and an enthusiasm for the new is desirable, especially when it is populated by keen minds honed on the challenge of attempting artificial intelligence. On the negative side of the computational metaphor we should bear in mind the recent privileged status of computation accorded by an age obsessed by information technology. Great importance has been attached to minor experiments and the reflections of AI researchers who were riding on the coat tails of a ‘new science.’ More importantly we should note what is left out by the computational view. Specifically this includes any ideas of social context; dialogue, reflection in action; social responsibility; the notion that human understanding operates through a background of shared cultural practices: or that design is a community activity rather than individual-centred. Design is seen in terms of variables, geometry, constraints, the vocabulary of formal systems. Operating within the rarefied realm of ‘the occurrent’ AI lacks formulae for value, quality, meaning. So they are dropped from the discussion. It is expected that these can be dealt with after we have been able to handle that which is readily formalized. So computational design is a party to the great divide perpetrated by Cartesian rationalism. On the one hand there is that which can be formalized and constitutes scholarship, but on the other hand there is the realm of common experience and private opinion that has a lower status. Historically, rationalism can be shown to be the basis of the split between objectivity and subjectivity, science and art, the rational and the romantic, the certain and the mysterious. Rationalism does not serve to unify the world within one great computational metaphor, but to divide its.“.

48

Coyne R D and Snodgrass A ‘Is designing mysterious? chaltengmg the dual knowledge thesis’. Design Studies Vol 12 NO 3 (1991) 124-131 49 Coyne R D, Rosenman MA. Redford AD, Balachandran M and Gero J S Knowledge-Based Design Syslems Addison Wesley. Reading MA (1990)

114

There is also the question of what motivates the study of design as computation. Not withstanding the difficulties of the task there is the promise of control suggested by the discipline. If we can put design into a computer then we can control it. This prospect affords a certain comfort to those who are perhaps intimidated by the apparent power of successful designers. Perhaps more troublesome is the power that can be exercised by research leaders over researchers in training. What better way to occupy novices and remind them of their inadequacies than to engage them in the challenge of automating intelligence? AI represents a valiant attempt to ‘implement’ the Cartesian ontology. AI relies on the storage of atoms of information, as data, facts, frames, scripts, or weights and thresholds, and the inference from these of behaviours, functions, and meanings”. How successful is this? Any AI

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1993

researcher knows only too well the limitations of the Cartesian ontology from first hand experience. It is worth reviewing some of the major problems here.

4.1

Th e problem

of infinite

rule regress

There appear to be several well known and successful automated (AI) rule-based systems, such as XCON for configuring computer systems”‘. Not withstanding the success of such systems, it is apparent that the domains within which they are applied have been very carefully circumscribed. The formulation of a rule-based system seems to involve defining the limits of a problem domain as much as it involves ‘codifying knowledge.’ Further, the ideal of throwing together a set of rules to account for human expertise is forever frustrated. There are never enough rules or procedures to capture even the simplest domain of human expertise. An expert system to select the door locks for a building seems to require yet more and more rules to account for every exceptional case and every eventuality. It would appear that the belief that such rule systems capture ‘expert knowledge’ is motivated by an underlying acceptance of Descartes’ account of knowledge: every accomplishment of man’s knowledge is linked by long chains of reasoning, and all knowledge is accessible in this way. However, Heidegger’s account suggests that we do not use rules. According to Polany?’ and Dreyfus’ we are in the realm of tacit knowledge or understanding. Rules come after the event as formal constructs within the realm of the ‘occurrent.’ Further, according to rules of law are applied rather than implemented. The Gadamer”, application of rules is a matter of human experience undertaken within a background of shared cultural practices. By way of contrast, the notion of implement&on suggests some mechanical matching of rule to situation. As indicated by Dreyfus’, explanations of human expertise in terms of the implementation of rules leads to an ‘infinite regress’ of rules. The implementation of each rule requires further rules to account for its implementation.

4.2

50

Bachant J and McDermott J R ‘Rl revisited: low years in the trenches’ A/ Mag Vol 5 No 3 (1984) 21-32 Polanyi M The Jacil Dimension Routledge and Kegan Paul. London (1987)

51

Th e problem

of control

layers

It is well known that it is possible to formulate some simple rules, that when implemented on a computer can generate a large and even infinite range of outcomes. Of interest to design is the capability of rule-based systems to generate patterns or geometrical figures. The AI researcher is interested in the use of such systems to generate particular patterns or outcomes, conforming to a set of requirements. The problem is defined as one of search. Search must be controlled in some way to inspect the space

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of outcomes systematically, to detect the desired outcome, to prevent the generator from hurtling down an infinitely long path, and most significantly to find the required outcome within a finite and useful period. Rules can be used to control such a system. These are search heuristics that are generally specific to the domain. The application of these heuristics can also be characterized as a search problem. It is a case of selecting the right rule for the right job. The selector of heuristics can similarly be described. It too requires a controller. Having effective control layers seems essential for anything like ‘intelligent behaviour.’ However, what arises is a system that requires more and more layers of control to effect some useful search task. Some AI research systems make these layers of control explicit. There are rules, tasks, strategies etc. The key to human intelligence is thought by some to lie in the use of such layering. But it seems not to work. The rules required for each subsequent control layer are increasingly difficult to formulate. Such systems often end with a simple ‘scheduler’ at the top level that contains an arbitrary ordering of procedures. It might be pleasant if they did, but these systems never take on a ‘life’ of their own, developing and exploring strategies, solving problems and generating designs. They usually fulfill the demonstration task for which they were designed and then lie inert and uncreative.

4.3

Th e problem

of machine

learning

Machine learning presents a similar problem. In keeping with the ‘wicked problem’ formulation of Archer”, the space within which design solutions may be found is ill-defined. This suggests that the operators or rules through which outcomes are generated are also undergoing change. The system needs to acquire the definition of its operators as it goes. It should be able to learn from its own activities and from other cases. The acquisition of design rules appears to be intractable. The ‘space’ of design exploration becomes even larger and more unmanageable.

4.4 52

Archer B L ‘An owlview of the structure 01 Ihe design process’ in Emerging Methods in Environmenfal Design and P/anring, (Ed G T Moore) MIT Press. Cambridge. MA (1970) pp 285307 Minsky M ‘A framework for representing knowledge’ in The Psychology ol Computer Vision P Ii Winston (Ed), McGraw Hill, New ‘fork (1975) pp 211-277

53

116

Th e problem

with frames

and categories

AI systems rely substantially on the notion of types and categories. Designs are often organized within categories. So there are different types of kitchens: L-shaped, U-shaped, galley kitchens and so on. These types can be represented in elaborate data structures called ‘frames’“3 that have default values for certain parameters, and methods or procedures for finding out or calculating values. Such frames can also be organized so that types (kitchen) and subtypes (galley kitchen) inherit certain properties of supertypes (room, space). Such hierarchies are notoriously difficult to formulate. Types, subtypes and supertypes seem connected in

Design Studies Vol 14 No 2 April 1993

non-tree-like ways. They mesh uncomfortably with component hierarchies, the instance-type distinction is often illusive, we are often interested in designs that appear to cross the boundaries between accepted types, and the structure of the whole conceptual system seems to be ever changing and dependent on the context of the projec?“. The reason for the difficulty is that we do not design or solve problems by organizing information into rigid hierarchical classification systems22. The formulation of useful classification systems seem to be dynamic, context dependent and beyond rule.

4.5

Th e problem

with neural

network

features

Neural networks present an alternative computational paradigm to those based on explicit symbol representation and manipulation55. The practical difficulties with neural networks are well known: the difficulties of verification, their poor theoretical base, the computational time required for even simple operations, and the queries hanging over the supposed biological bases of the technology’“. A further problem that they bring to light is the question of what is afeature. Input and output units of a neural network must map onto some descriptive feature. In the case of the kitchen example, these include the presence or absence of a breakfast table in a kitchen, or another attribute. Of course, then we need to consider how a feature is identified. There is no theory of feature detection. The identification of features is an interpretative activity. There appear to be no domain independent atoms of existence that can be mapped onto the input or output units of a comprehensive neural network.

4.6 54 Coyne

R II ‘Design reasoning without explanations’. A/ Msg. Vol 11 No 4 (1991) 72-80 Rumelhart D E and McCletland J L (Eds) Pa&/e/ Disfribuled Processing: Explorations in fhe Microstructure of Cognilion. Volume 1: Foundations. MIT Press. Cambridge. MA (1997) Newton S and Coyne RD ‘The impact of connectionist systems on design’ in Arlilicial InfeW gence in Design 97 (Ed) J S Gero, Butterworth-Heinemann. Oxford (1991) pp 49-75 Minsky M ‘Logical versus analogical or symbolic versus connectionist or neat versus scruffy’ A/ Mag Vol 12 No 2 (1991) 34-51

55

56

57

Th e problem

with metaphors

of the society of mind

Because of the complexity of design as computation, some think that the answer lies in combining AI techniques”‘. Success will ultimately he in the ability to integrate a wide range of approaches. So it is common to formulate AI systems as made up of independent agents, objects or knowledge sources that must somehow communicate with one another. It is thought that this can be achieved through a common medium such as a ‘blackboard’ or they may communicate directly by broadcasting or sending ‘messages’ to other agents. The agents represent different bodies of expertise and they have to resolve amongst themselves which agent is to act and in what order. They may act autonomously or be under the control of a scheduler. Even where there is no such specific structure to an AI program this idea of an AI system as a society of autonomous agents also serves as a way of conceptualizing the system. Common agents to be found in such systems are: scheduler, monitor, knowledge source, knowledge acquisition facility, explanation facility, design generator, constraint

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checker, backtracking provoker, case-based reasoner, case recorder, critic, modifier, mutation operator, geometry interpreter. message handler, facts base, goals base, and conflict handler. These agents usually feature on complex diagrams with many linkages and arrows giving some indication of how these communicate with each other. Minsky” has posited that the mind is such a multi-agent processor. The difficulty of implementing such systems is well known to AI researchers. It seems that the idea of experts sitting around a table and communicating with each other to solve a problem cannot be translated into the idea of ‘computational agents’ communicating in a machine. Human communication is not simply an information processing exercise of sending and receiving messages. Cooperation between a group of people is but a small part of something even larger: their participation in all humanity with its languages, norms and social practices. Reddysx has shown how the information processing theories of Shanon and Weaver have contributed substantially to misunderstandings about communication. What appears as an arrow expressing communication between two boxes on a diagram turns out to be a complex interplay of context specific exchanges within a background of shared and changing cultural practices. Natural language has eluded AI techniques. It is unlikely that the depiction of a complex of communicating agents solving a problem will be any easier. The list of problems could be extended further. In summary, there are two questions (with variations) that can be asked of any AI project to good effect: ‘How do you handle the combinatorial problem?’ and ‘How do you handle conflicts among X?’ where X is any of the following: rules, goals, actions, constraints, knowledge sources, agents, operators, messages or actions. It is well known that all practical problems formulated symbolically are NP-complete, so there are going to be difficulties, particularly when the simple problem formulation is scaled up to something useful. Similarly it is well known that there is no theory of integration or conflict resolution. In the same way that it is possible to identify how to criticize a project it is possible to identify how to generate one. A glance through the proceedings of any design conference committed to AI suggests that what is required is an acronym, perhaps a logo, a complicated flow diagram and a screen display. A computer program is advisable but not mandatory, especially with the advent of HyperCard.

58 Reddy

M ‘The conduit metaphor - a case of frame conflict in our language aboul language’ in Metaphor and Though1 (Ed) Ortony A Cambridge University Press. Cambridge (1979)

118

A great source of frustration for AI researchers is the constant advancement of the frontier of understanding: not that more is understood, but that the frontier of understanding moves further and further away. As if

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1993

logic programming and linear resolution were not sufficient it transpires that what is required is truth maintenance to handle human inconsistency. Having come to terms with case-based reasoning’” and frames it is then apparent that parameters need to be mutated.“” As logic and symbolic computation are inadequate it appears that it is necessary to understand the entropy behaviour of multilayered back-propagating neural networks”. The future does not look any simpler. According to Minsky ‘we’ll make our projects more ambitious. Designing an artificial mind will, be much like evolving an artificial animal.“i There will be ‘organs’ (or agents) arising ‘genetically’ and communicating with each other under the control of agencies watching and rewarding each other, and in addition, ‘as in any society, there must be watchers to watch each watcher, lest anyone or a few of them get too much control of the rest.“’ The greatest problem facing the computational metaphor is simply that it is founded on a demonstrably limited epistemology and ontology. Unwittingly AI provides ample support for a Heideggerian view. The failure of AI to account for the rich panoply of human experience provides evidence for the inadequacies of the Cartesian ontology. The complex seems not to be derivable from the simple. The whole does not emerge from a consideration of parts. In spite of the elegance of the reductive approach, meaning, let alone intelligent behaviour, seems to evade the process. If it worked then artificial intelligence would have been at least partly realized. According to Dreyfus’-’ ‘AI research has called the Cartesian cognitivist’s bluff.’

5 Conclusions

59 Stanfill C and Waltz D ‘Toward memory-based reasoning’ Comm ACM VoI29 No 12 (1966) 1213-1226 60 Gem J S and Maher ML ‘Mutalion and analogy IO support creativity in computer-aided design Proc. CAAD Fukms ‘91 (1991) pp 241-249 61 Minsky M ‘Logical VB~SUS analogical or symbolic versus connectionisl or neat versus scrully’ op. cit., p 49 62 Ibid., p 60 63 Dreyfus Ii L Seing-in-fheWorld. op. cit.. p 119

The world is a better place once the thin veil of rationalism has been removed. It transpires that we are more involved in the world than rationalism suggests. There is no standing back to an objective position and seeing the world ‘as it really is,’ except as part of a particular and useful game (science) that has its own evolving rules. To stand back and apart from the world, in the Cartesian manner, is to appeal to a particular kind of metaphor structure for a particular purpose. We are no closer to the ‘essential nature’ of the ‘material world’ through scientific observation, formulae and rules than we are through a poem. If this line of argument is followed through then it can be shown that we can also dispense with certain vexing philosophical constructs such as the ‘noumenal world’ that is immune from real knowing, as well as foundationalism, idealism and relativism’. Where does this leave the study of design? Clearly it is designers who design. Design is not a rule-based or symbol processing activity. An

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understanding of design should begin with the experience of design. This is not the experience of empirical protocol studies, but the experience understood through the exchange of ideas on design. The test question will always be ‘is this how design appears to you?’ Of course the question fits within the context. What do we wish to do with our explanations? The means at our disposal for offering plausible explanations of design are those of any philosophical study: the devices of rhetoric and persuasion; the appeal to appropriate and moving metaphors; and an appeal to the accepted means of validation of the particular discipline. Some alternative (nonrationalistic) propositions about design experience that have been posited are present here: i) ii) iii iv) v) vi) vii)

Reflective conversation with the materials of a design situation Bringing experience to bear in making judgements Projecting expectations into a situation Engaging in dialogue with a situation Engaging in the play of metaphors Sharing cultural practices Engaging the hermeneutical (interpretative) circle

The first proposition i) is the characterization presented by SchonhJ. The usefulness of the rest of the propositions ii)-vii) have been argued through in detail elsewhere”‘.‘s. We should note that in subjugating rationalistic explanations we are not handing our deliberations over to the realm of the mysterious. In fact, the reverse is the case. In bringing reflection, experience, judgement, interpretation, cultural practices and metaphor to the fore, we are considerably widening the scope of design understanding and allowing the areas of design experience excluded by rationalism into the discourse. The idea of mystery is promoted through exclusion.

64

SchOn D The Rs//ecfive Pracfilioner. MIT Press. Cambridge. Massachusetts (1993) 65 Snodgrass A and coyne R D ‘Is desigmng hermeneulical?’ Workmg Paper. Faculty of Architecture. University of Sydmy. Sydney (1990)

120

From some agreed understanding of design experience the question now arises: what is the role of the computer in design? In general terms the role should be to enhance design experience in some way. This is a role that is now being addressed in practice in several important ways, that include databases, text processing, drawing and modelling systems, image processing, communications and various kinds of calculation. How may we characterize a nonrationalistic orientation towards the role of computation in design? The following list is suggested as the start of such an inquiry. 1) The first concern is to ‘clean up’ the language of AI applied to design. This may mean dispensing with the language of modelling in relation to human behaviour. The ideas of ‘user models’ of artifacts of human

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knowledge are unsustainable. It is not possible to ‘capture’ in a . computer something that corresponds to the way people see things or apply their understanding to solve problems. So too with the quest for the depiction of meat~ing in a computer program. The manipulation of logical propositions stored in a computer is simply an advanced form of text processing. It does not represent a transition through an ‘ontological hierarchy’ from data to meaning. In Heideggerian terms the meaningful is not something derived from the ‘pure occurrent.’ Having dispensed with the ontological primacy of a material world we are no longer compelled to think that meaning is so derived., 2) A second lesson is to assert the primacy of metaphor in understanding computers and our relationship with them. Many vexed questions tossed about within the AI and cognitive science community lose their punch when seen metaphorically: ‘Can machines think?’ ‘Can we capture human knowledge and creativity?’ ‘Will machines ever exhibit consciousness?’ In the same way that we are able to describe our own activities in terms of mechanism (and scales and tools etc) we are capable of explaining equipment in terms of ourselves (‘the machine went berserk,’ ‘the program sees the problem differently,’ ‘the computer knows how to handle it’). There is no difficulty with this except that those operating within the Cartesian ontological framework tend to see such statements as other than metaphorical. 3) It should also be clear that there is a demarcation between equipment and people. Because rationalism tells us that reason is essentially computation, there is a tendency to see computational devices as more than equipment. So a computer system that embodies the rules or programs of its creator or ‘domain expert’ is seen as some kind of autonomous satellite of human intelligence. This apparent blurring of the human-machine relationship is a source of considerable confusion. The Heideggerian alternative is to assert that computers are and always will be merely equipment. One of their major roles is to facilitate communication. As symbol processors they allow one individual or group of people (the designers of a particular item of software) to communicate with another group of people (the users). Computers do not capture human reasoning but are a means by which designers can enable others to accomplish some task. It happens that their medium for this enablement is not a telephone, but a computer system. All design can be seen in this light. Software serves as a means of collaboration among designers. The designers of design-based software are involved in enabling other designers. 4) In asserting the central role of people in the computing enterprise it should be apparent that people want to be and should be in control. With any item of equipment: such as a car, a pocket calculator or even

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a dinner plate: people seem interested in knowing its limitations before committing themselves to it. They require an overview. The last thing users want from an item of equipment, such as a programmed computer system, is unpredictable behaviour, no matter how ‘intelligent.’ 5) Heidegger’s ontology suggests an approach to the design of computer systems. Winograd and Flare? have taken up the theme that design should accommodate our primary mode of experience which is the realm of the available, the inconspicuous. So computer systems should allow for ‘transparent’ habitual use. An ‘intelligent’ CAD system that constantly intrudes into the realm of the familiar with requests to resolve a conflict between two of its ‘agents’ will hardly be welcome. This theme of designing for the ‘available’ is discussed in the context of architectural design by Coynebh. 6) As with any design task the development of the role of the computer in the design process is itself an evolving, shared process requiring prototyping, developing and evaluating. No one has yet been able to predict successfully what style of software or CAD system will ‘catch on.’ For every success there have been any number of failures. There is no such thing as an objective set of user needs that have to be met. Needs evolve and develop in conjunction with software and as contexts change. The design of ‘available’ software will always be an ongoing project. 7) There is always the laudable ambition that computing can do more for designers than is now possible. How can computers assist further in enabling designers in their reflection, experience, judgement, interpretation, cultural practices and use of metaphor? Clearly computers are invaluable for facilitating collaborative design and communication between parties involved in the process. Rapid access to information is important in enhancing the designer’s ability to make effective judgements. The recording of design experience in terms of textual accounts and pictures is obviously important. The presentation of codes and rules for interpretation and application by the designer may be helpful. Various tools and media can be shown to be enabling in different ways in the context of design. Drawing and clay modelling each assist the design process in different ways. The computer opens up an enormous range of possibilities as a medium for designers. Finally, if design is characterized as ‘metaphor play’ then the computer opens up possibilities for exploring design metaphors. 66

Coyne R D ‘Inconspicuous arch8tecture’ Prcc. Gadamsr, AClion and Reason. University of Sydney, Sydney (1991) pp 62-70

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What then is the role of current AI tools and techniques, such as the depiction and manipulation of rules, frames, logic statements, networks and cases? Provided that this is seen simply as the application of symbol

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processing and text processing, then there is no difficulty. The difficulty lies in elevating this process to something akin to reasoning. The test for such systems will be whether or not they produce something useful for designers. It is generally assumed by AI researchers that because these systems bear some resemblance to human cognition then we have an affinity with their operations. So we are content to be asked questions by an expert system as it ‘reasons’ through its rules and we will trust the logic of its outcome. The best way to redeem the technology from the danger of these assumptions is to make the technology as transparent as possible. In the same way that statistics is of most value to people who understand how statistics operate, we may suppose that systems that process logic statements will be most useful to people who understand symbolic logic and understand the logic bases of the systems. This militates in favour of computer systems that are designed so that they divulge their inner workings. It also suggests that such tools will be most valuable to people who understand the technology at the outset.

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