- Email: [email protected]
Thoughts on a computer-based design apprentice
(~rnput. & Graphics Vol. 14, Nos. 3/4, pp. 511-517, 1990
0097-8493/90 $3.00 + .00 © 1990 Pergamon Press plc
Printed in Great Britain.
Chaos and Graphics T H O U G H T S ON A COMPUTER-BASED DESIGN APPRENTICE MICHAEL ECKERSLEY University of Maryland, 1401 Marie Mount Hall, College Park, MD 20782 Abstract--Formal grammars in design are discussed. A design tool is proposed, consisting of an original design production system based on the interplay of stochastic processes and shape generating algorithms. Graphic designs generated by the system are presented.
INTRODUCI'ION
The impact of computer technology on the practice of design to date has been remarkable but largely confined to technical or production-oriented tasks; the principles and underlying assumptions of design remain largely untouched by comparison. However, we may soon see computers used in unprecedented ways in the early stages of design. The notion of a designer using a computer to get and develop design ideas is not inconsequential. If the notion is realized, the proliferation of such systems will almost certainly raise controversy. In any case, it will have a significant impact on the meaning and practice of design. The feasibility of such claims is implicit in the work of Kitsch[I], Koning and Eizenberg[2], Stiny and Mitchell [ 3 ], and others. Their work to describe, algorithmically encode, and computer-simulate design styles of certain artists and architects is interesting on an intellectual level. What makes it compelling is the ability to synthesize original designs within a style (i.e., "new Diebenkorns," "new Frank Lloyd Wrights"). Borrowing on such techniques, creating a few new ones, and making them accessible through a friendly graphics user interface will conceivably lead to the creation of the first actual computer-based design tools. The key here is to find an easy way for designers to articulate aspects of their personal design aesthetic (shape grammar) to a computer, allowing the computer, in turn, to generate a set of designs based on those instructions. Such systems could effectively function as the designer's "apprentice," obsessing on the production of variations within a style and freeing the designer to concentrate on defining new style parameters and creative possibilities. A program of design can be thought of as a series of goal-directed decisions / actions out of which an artifact is formed. In the truest sense, the essential "design" of an artifact (e.g., automobile, poster, computer program) is not so much an aspect of its formal or functional attributes as it is a program of decisions/ actions that yielded the artifact. Design is, by definition, nonrandom. Yet, ironically, what this paper will show is that randomness, when interfaced with rules controlling parameters of shape, transformation, and spatial organization, can offer a powerful point of departure in the early stages of the design process. The theoretical context of a "design apprentice," the potential
uses of the technology, and the change it implies from the status quo will also be discussed. DESIGN AND RANDOMNESS
Designing is one of the more complex and strongly goal-directed human cognitive activities[4]. However, since design problems tend to exhibit poorly specified initial conditions, allowable operations and goals, they are among the most ill-defined of problems[ 5 ]. As a subset of general human problem solving, designproblem solving is a process of clarifying the problem, searching for information relevant to its solution, and formulating an acceptable solution within a defined context or problem space[6]. The unifying aspect to the various disciplines wherein design plays a central role (e.g., art, product design, architecture, graphic design, composition of all types, etc.) is the pursuit of novel and functional form. Even so, the defining criteria of "functionality" can vary considerably from discipline to discipline. A limited definition of the designed artifact, but one adequate for the present discussion, suggests a vocabulary of shapes occurring in a field and a set of spatial relations between those shapes [ 7 ] --shape being defined as a finite collection of lines, vocabulary being a set of unique shapes, and spatial relation being a collection or arrangement of shapes. The context of this discussion is roughly limited to design aesthetics: the consideration of beauty and novelty of shape, form, and composition. Functional aspects of design beyond these are not necessarily beyond the scope of the methods presented, but will have to be addressed on another occasion. Randomness is commonly regarded as uninteresting and of little use. My assertion, however, is that something as uninspiring as a table of random numbers found in the appendix of any statistics book can be the source for virtually unlimited graphical richness and usefulness. Fig. 1 shows a computer-generated random array based on 10,000 "coin tosses." The motif or shape indicated at bottom-left was inserted and rotated 45 degrees with each occurrence of heads; the probability is ½ and the distribution is largely homogeneous or even. Despite the simplicity of the generating algorithm, the complexity (information content) of the array exceeds that found in the intricate patterns of Truchet [ 8 ] or even the aperiodic tilings of Penrose [ 9 ]. Interestingly, the local areas of the array are far from
511
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MICHAEL
ECKERSLEY
S
/ / m
m
/ Fig. 3. Random distribution with linear shape motif (probability l ). This array is generated from the same series of random numbers as in Fig. l; the only difference being the use of a linear motif.
h o m o g e n e o u s (Fig. 2). G i v e n a n infinitely squared b o u n d a r y , the array would contain a virtually infinite diversity o f negative shapes or voids ( " n e g a m o r p h s " ) . T h e array can be described as globally stable but locally
unpredictable, asymmetrical, a n d visually dynamic. Fig. 3 shows the clear topological differences yielded from the same "coin-toss" algorithm using a different motif. O n e can easily scan a r a n d o m array a n d ascribe to isolated areas a n aesthetic value (see Fig. 4). To summarize, by defining one or m o r e shape rules a n d linking the r a n d o m occurrence of particular n u m bers to the invocation o f a few predefined shapes, one can reveal as m a n y u n i q u e structural " c o m p o s i t i o n s "
Fig. 2. Detail section of Fig. 1. While the array is not fractal, close-up views reveal a plethora of graphically complex white and black shapes. Given an infinitely squared boundary, such shapes are virtually unlimited.
Fig. 4. Detail section of another random distribution (probability ½). This array is generated from the same series of random numbers as in Fig. I. Here a triangular motif was inserted and rotated. Compositions similar to this can be found in virtually any foundation level two-dimensional design course. It shows some rather interesting areas of figure-ground reversal, and can be ascribed aesthetic qualities.
Fig. 1. Distribution of shape motifs generated from 10,000 random numbers (probability l ). The motif indicated at bottom-left was inserted and rotated 45 degrees with each occurrence of heads.
Computer-based design apprentice as there are algorithms, shapes, degrees of randomness, and methods of graphical analysis. Paradoxically, such compositions need not, and often do not, appear random. This is likely due to the misconceptions most people have about randomness, and the fact that randomness often seems to "behave" nonrandomly[10, 11]. Waddington [ 10 ] expressed the principle of random variation in biological systems, asserting that organisms turn randomness into order by virtue of their own principles of dynamic organization. Thus the source of heritable variation can be traced not only to random factors, but also to the inherent properties of the generative system[13]. The binding of randomness through rules or controlling parameters yields stochastic structures that are no longer random in the truest sense (if any pure manifestation of randomness is visualizable ), but that maintain an ingredient or imprint of randomness. Thus randomness is naturally creative when it impacts upon systems, causing great diversity based on a common "system theme" (e.g., snowflakes, facial characteristics). Analogous system constraints account for the family of rather exotic aggregate negative shapes in Figs. 1 and 2. Further and more elaborate extensions of the principle can be found in writings on cellular automata theory[14, 15 ], and the Game of "Life" [ 16 ]. Some design theorists have labored to devise deterministic problem solving models with the goal of factoring out chance and variation in the solution to design problems. These methods are intended to prevent any deviation from the optimal design solution. Unfortunately, these efforts usually prove self-defeating for very simple reasons. In real life the interaction of multiple factors usually transforms the problem before its own solution. Virtually any design problem, no matter how simple or highly constrained, will evoke as many solutions as there are designers, or occasions at which a single designer sets out to solve it. What emerges, however, from the often diverse solutions is a common set of overriding design concerns and a set of visual / spatial elements (a visual language) based on a universe of forms (a shape vocabulary) that we all know something about. DESIGN MACHINES AND THE POWER OF GRAMMAR
The notion of a design machine, considered by some to be a contradiction in terms, raises important questions about what constitutes design and to what degree, if at all, the process can be automated. The first serious mention of a design machine was made by Negroponte [ 17 ], and later taken up by Stiny and March [18]. The theory, as treated by Stiny and March, tied into a body of research going back to the 1950s involving formal grammars. The concept of grammar had been used by philosophers and linguists to describe the governing rules or structure of language. Chomsky [ 19 ] showed the possibility of describing and operationalizing grammar by the use of computers. By 1964 the success achieved from such techniques in linguistics led Kirsch [ 20 ] to apply the principle to shapes
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found in pictures and designs. Stiny and Mitchell [ 21 ], Gips [ 22 ], Lauzzana and Pocock-Williams [ 23 ], and others have since popularized and elaborated on research involving shape grammar and applied it to the analysis of architectural design. Most notably Koenig and Eizenberg [ 24 ] conducted an elaborate analysis of what constitutes "style" in the architecture of Frank Lloyd Wright. Koenig and Eizenberg developed a shape grammar for a sample of "prairie-style" houses designed by Wright, and from this grammar they were able to generate three new designs based on the grammar. This most impressive application of shape grammar theory to date has strong implications for the critical and historical study of Wright's architecture, and, for that matter, of architectural design in general. The application of shape grammar theory by Kirsch and Kirsch[25] is of a similar vein; they set out to identify the underlying formal characteristics of a series of modern nonobjective paintings by Richard Diebenkorn. From these characteristics Kirsch and Kirsch inferred a set of rules to account for the common features in the series. Once the features were identified, the task was then to determine the appropriateness and sufficiency of the grammar by (a) demonstrating that any of Diebenkorn's original works from the series could be recreated using the grammar, and (b) generating a series of original compositions based on the grammar and then checking for consistency with the original set of Diebenkorns. Essentially, what each of these production systems amounts to is a kind of design machine consisting of a narrowly constrained knowledge-base and a generator capable of instantiating the rules of the grammar. The strength of these systems lies in their ability to "flesh out" the set of graphic configurations from the possibilities extended by the grammar. Their built-in limitation, on the other hand, is the fixedness of their database. The tools were never intended for creative experimentation in the hands of artists and designers. Imagine, however, if such technology were developed for creative experimentation. Imagine further such tools in the hands of a young Wright or Diebenkorn still actively formulating a personal grammar, still searching for ways to reinvent design. A technology that provides the ability to scan dozens or hundreds of possible outcomes to a given design problem could be intimidating, but it could also be informative in ways traditionally unimaginable. One might make the case that a great mind is facile enough to visualize the vast number of possible configurations extending from a grammar. Unfortunately, most of us are not so blessed. We can assume, however, that the informed novice and professional are quite capable of identifying promising configurations from less-promising ones. Therefore, the goal seems to be one of making valid production systems accessible to these individuals so they might more effectively do what they do best: identify and develop the set of really good design options from the universe of all possible design options. If the creative individual is, by definition, both fluent and flexible in a problem solving context, a pro-
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MICHAEL ECKERSLEY
duction system that can show the breadth and depth of design options would, almost certainly, be an asset to that individual's creative potential. THE DESIGN APPRENTICE
Work by the author on a rule-based interactive design production system varies considerably in scope and intent from those production systems highlighted above, and it rests on the following assumptions: I. Practical shape production systems can be developed using existing relational graphics software; these systems can effectively function as moderately "intelligent" design tools. 2. From an aesthetic context, a designer is only as good as the sophistication, versatility, and novelty of his /her own shape grammar. Use of an interactive shape production system is likely to heighten a designer's awareness of, and experimentation with, style parameters and meta problem solving principles. 3. Randomly configured shapes in a given format space often provide a better point of entry to a design process than does a "blank canvas." 4. For production systems in design to be both useful and practical, they must be open-ended in order to provide as many options as possible to the designer. It is the designer's task to define system parameters, however broad or narrow, even if they conflict with established precedents. Called "Design Apprentice," the production system was written in Apple's Hypercard environment, and it demonstrates the potential for creating powerful and easy-to-use design tools. The purpose of Apprentice is to be a tool for design experimentation and creative discovery. For this reason its control structure and graphics capabilities differ considerably from other shape productions systems; its processes are far less linear and its grammar is variable and less explicit. Design Apprentice is loosely based on the masterapprentice model. As such, its goal attributes are to: • be easily accessible to the designer via a common language • represent the master's design-related beliefs and values • follow instructions implicitly • be a sounding-board for ideas • accumulate and retrieve relevant information • work independently to experiment with and test new visual ideas • exhaust all plausible design possibilities extending from a grammar • work hard and fast • be fun to work with. While preliminary versions of Apprentice don't measure up to some of these desired qualities, with development in the hands of a designer, a surprising number of goal qualities are attainable. In this respect the de-
signer's responsibilities are to teach freely and develop the production system, to articulate as clearly as possible aspects of what he/she knows, and to remain open to the system's feedback. The emphasis here is on a shape grammar development tool that, through interactive use and tinkering, takes on many of the designer's own embedded preferences--not to clone a designer's grammar but to tease it and, perhaps, feed it. Apprentice enters into this relationship as little more than a blank slate with an innate vocabulary of shape primitives (line, shape, texture, etc.--most of which are packaged within Hypercard itself) and a default architecture for following rules through a series of button-invoked scripts accessing the graphics tools. The relative complexity and breadth of the rules and graphical parameters are defined by the mentor. Execution of the rules within the scripts is rapid, making it easy to see the immediate graphical outcome. Apprentice is a Hypercard "stackware" application that runs on any Macintosh with at least IMB of RAM. It consists of three primary background modules: Tablet, Portfolio, and Help. Navigation within and between modules is done by pressing screen buttons using a mouse (see Goodman's[26] book for an in-depth description of the Hypercard relational environment). Tablet is the business-end of Apprentice where shape rules are invoked by button and modified by accessing and changing button scripts. Buttons in Apprentice access the Macintosh graphics tools and allow the user to customize shape-generating algorithms and define general parameters of line size, shape type and size, type font and size, transparency or opacity, etc.--this with only minor knowledge necessary of the relatively transparent Hypertalk programming language (Fig. 5). These changes become the new default parameters until they are redefined by the user. The user can initiate commands one at a time (with the freedom to undo the most recent command) or automate the process, iterating multiple rules consecutively. Apprentice may also be programmed to generate any number of compositions automatically in the designer's absence, storing each composition in Portfolio for later use, adaptation, a n d / o r disposal. Button scripts in Apprentice will also allow for configurations of two-fold and fourfold symmetry. Importantly, the user has continual manual access to the graphics tools except at times when Apprentice is involved in a loop, at which time the user may break-in and assume control. The user may also designate formally or informally within Apprentice the degree of graphical randomness desired, and vary that value instantly. Figs. 6, 7, 8 and 9, 10, l l show the graphic diversity of designs produced through just two of Apprentice's button scripts. DISCUSSION
Researchers working on computerized production systems in design often adopt the agenda of A.I. scie n t i s t s - t h a t being to extend the concept of machine emulation to its ultimate conclusion by taking the human "out of the loop." Aside from the tantalizing intellectual and computational challenges that this goal
Computer-based design apprentice on mouseDown repeat unt il the moueeCl i¢lt put 50,75, 100,125,150,175,200,225,Z50,275,500,525"T'] into xroords put 15,'i0,65,90,I 15, I'~0,165,190,2'15.2q0' mto ycoords put any item o! txeoords) into XI put any item ol (ycoofdSi into YI put XI & "," & YI into upLeft put kl into X2 put random of 10 into Ipool
515
nition, limited and limiting. Computers already inflict a confining influence on designers, and this is often evident in the stiffness of their work. If production systems only function to induce narrow thinking in designers, then such systems are exactly what we d o n ' t need. In the short term, and based on an altogether different set of priorities, production systems like Design
i! lpooh-'~ then add 50 to X2 put YI mtoY2 add 50 to Y2 else d ]pooh-6 then add 100foX2 put YI Into ~2 add 100 toY2 else i[ lpoOl-.5 then add 150 toX2 put YI ,nto Y2 add I.'fO to Y2 e.d d put X2 & ....& Y2 into loRight put random of 2 into Zpool if Zpool-I then choose rectangle tool else if Zpool-2 then choose oval tool end if get line$ize set lineSize to random of ~t drag irom upLelt to IoRight wait I put random o| -t into pool if pool-I then set idled to true set pattern to 12 doHenu transparent
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end repeat choose browse tool end mouseDown Fig. 5. Apprentice's Option 1 button script. Figs. 6-8 were generated from this script.
presents, there is also the worrisome possibility that the commercial production o f " e x p e r t design systems" will be marketed to consumers as the final word in design. If such systems were based on the g r a m m a r of Charles Eames or Michael Manwarring, for instance, consumers might assume mistakenly that they were purchasing the perpetual services of these gentlemen. Thus while attainment of this extended goal will require more time and technical development than most researchers are willing to admit [ 27 ], when such systems are developed they might become the equivalent of M Y C I N or D E N D R A L , but used without the wariness or controls that are a part of expert medical diagnosis. Stand-alone systems with a fixed rule-base (as some design researchers would have us build) are, by deftCAG 14:3/4-J
Figs. 6-8, Designs generated from Apprentice's Option 1 Button Script. Grid parameters and variables of shape, texture, line width, "filledness," and probability were defined intuitively by the author.
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MICHAELECKERSLEY
Figs. 9-10. Designs generated from Apprentice's Option 4 Button Script. Here variables of type font and point size are thrown into the mix, as well as a symmetry variable (believe it or not) and an invert algorithm. These designs were taken from a series of 25 compositions generated in about five minutes time. They were each extracted from a larger compositional field and pasted into Portfolio.
Apprentice could become a constituent part of virtually any computer graphics software, including CAD, functioning to foster novelty and the synthesis of diverse forms. With presently existing software technology, artists and designers with virtually no computer programming experience may be enabled to define shape/action rules intuitively and, in turn, transform random number streams into graphical compositions of great variety and complexity. Compositions generated by Apprentice showing limited design promise can be easily "canned," while compositions possessing
areas of interest can be adapted to create designs of considerable aesthetic sophistication. Candidly speaking, at least two of every ten randomly generated designs are often better than the designs of many trained designers (when constrained by the same design parameters). What does this mean? First, it means that stochastic compositions appear quite different from what most people expect randomness would yield (i.e., such compositions often appear "designed"). Second, few designers and design educators have compared compositions generated sto-
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Fig. 11. Six Designs generated from Apprentice's Option 3 Button Script. Randomized design using twofold reflectivesymmetry as a variable. Some comparison can be drawn between these and similar decorative designs by Frank Lloyd Wright.
Computer-based design apprentice chastically with compositions developed m a n u a l l y by individuals working essentially the same design problem [28 ]. In conclusion, design p r o d u c t i o n systems s h o u l d n ' t be regarded as a threat to the designer, b u t rather as a potentially useful design tool. After all, such systems m a y someday exist as a constructive challenge to students a n d educators alike, saying: " Y o u d o n ' t have to start from scratch with each new d e s i g n - - w h y not just improve incrementally on what r a n d o m n e s s gives you? If you want to push the b o u n d a r i e s of a design style, or switch styles completely, simply work higher up the ladder on the level o f rules a n d probability.
REFERENCES
1. R. A. Kirsch, Computer interpretation of English text and picture patterns. 1EEE Transactions on Electronic Computers 13, 363-367 (1964). 2. H. Koning and J. Eizenberg, The language of the prairie: Frank Lloyd Wright's prairie houses. Environment and Planning B 8, 295-323 ( 1981 ). 3. G. Stiny and W. J. Mitchell, The grammar of paradise: On the generation of Mughul gardens. Environment and Planning B 7, 209-226 (1980). 4. H. A. Simon, The Sciences ~fthe Artificial. MIT Press, Cambridge, MA ( 1971 ). 5. H. A. Simon, The structure of ill-structured problems. A rti[icial Intelligenee 4, 181-201 ( 1973 ). 6. N. Cross, The relevance of cognitive styles in design education. Design .~lethods and Theories 17(I), 37-49 (1983). 7. T.W. Knight, Languages of design: From known to new. Environment and Planning B 8, 213-238 ( 1981 ). 8. C. S. Smith, The tiling patterns of Sebastien Truchet and the topology of structural hierarchy. Leonardo 20(4), 373-385 (1987). 9. B. Grunbaum and G. C. Shephard, Tilings and Patterns, W. H. Freeman and Co., New York (1986). 10. R. Falk, The perception of randomness. Paper presented at the Fi[?h Conference of the International Group .for Psychology of Mathematical Education, Grenoble, France (1981).
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11. K. H. Teigen, Studies in subjective probability I: prediction of random events. Scandanavian Journal of Psychology 24, 13-25 (1983). 12. C. H. Waddington, The Strategy of the Genes, George Allen and Unwin, London (1957). 13. P. T. Saunders, The evolution of form and pattern. Leonardo 22( 1), 38-39 (1989). 14. J. von Neuman, Theory of SelJ:Reproducing Automata. (Text was authored by Wolfram, edited and completed by A. W. Burks.) University of lllinois Press, Urbana (1966). 15. S. Wolfram, Theory and Applications ~f Cellular Automata, World Scientific Publishing Co. Pte Ltd., Singapore (1986). 16. E. R. Berlekamp, J. H. Conway and R. K. Guy, Winning Ways. Vol. 2, Chapter 25, Academic Press, London (1982). 17. N. Negroponte, The Architecture Machine, MIT Press, Cambridge, MA (1970). 18. G. Stiny and L. March, Design machines. Environment and Planning B 8, 245-255 ( 1981 ). 19. N. Chomsky, Aspeets of the Theory ~] Svntax, MIT Press, Cambridge, MA (1965). 20. R. A. Kitsch, Computer interpretation of English text and picture patterns. IEEE Transactions on Electronic Computers 13, 363-376 (1964). 21. G. Stiny and W. J. Mitchell, The grammar of paradise: On the generation of Mughul gardens. Environment and Planning B 7, 209-226 (1980). 22. J. Gips, Shape Grammars and Their Uses, Birkhauser, Basel (1975). 23. R. G. Lauzzana and L. Pocock-Williams, A rule system for analysis in the visual arts. Leonardo 21 (4), 445-452 (1988). 24. H. Koenig and J. Eizenberg, The language of the prairie: Frank Lloyd Wright's prairie houses. Environment and Planning B 8, 295-323 ( 1981 ). 25. J. L. Kirsch and R. A. Kirsch, The structure of paintings: Formal grammar and design. Environment and Planning B 13, 163-176 (1986). 26. D. Goodman, The Complete Hypercard Handbook, Bantam Books, Toronto (1988). 27. M. Eckersley, On heuristics and design-problem solving behavior: Cracking the "black box." Design Methods and Theories 24(3) (1990). 28. M. Eckersley, Rules, randomness and compositional structure in design. Leonardo 23 ( 1 ) ( 1990 ).
0097-8493/90 $3.00 + .00 © 1990 Pergamon Press plc
Printed in Great Britain.
Chaos and Graphics T H O U G H T S ON A COMPUTER-BASED DESIGN APPRENTICE MICHAEL ECKERSLEY University of Maryland, 1401 Marie Mount Hall, College Park, MD 20782 Abstract--Formal grammars in design are discussed. A design tool is proposed, consisting of an original design production system based on the interplay of stochastic processes and shape generating algorithms. Graphic designs generated by the system are presented.
INTRODUCI'ION
The impact of computer technology on the practice of design to date has been remarkable but largely confined to technical or production-oriented tasks; the principles and underlying assumptions of design remain largely untouched by comparison. However, we may soon see computers used in unprecedented ways in the early stages of design. The notion of a designer using a computer to get and develop design ideas is not inconsequential. If the notion is realized, the proliferation of such systems will almost certainly raise controversy. In any case, it will have a significant impact on the meaning and practice of design. The feasibility of such claims is implicit in the work of Kitsch[I], Koning and Eizenberg[2], Stiny and Mitchell [ 3 ], and others. Their work to describe, algorithmically encode, and computer-simulate design styles of certain artists and architects is interesting on an intellectual level. What makes it compelling is the ability to synthesize original designs within a style (i.e., "new Diebenkorns," "new Frank Lloyd Wrights"). Borrowing on such techniques, creating a few new ones, and making them accessible through a friendly graphics user interface will conceivably lead to the creation of the first actual computer-based design tools. The key here is to find an easy way for designers to articulate aspects of their personal design aesthetic (shape grammar) to a computer, allowing the computer, in turn, to generate a set of designs based on those instructions. Such systems could effectively function as the designer's "apprentice," obsessing on the production of variations within a style and freeing the designer to concentrate on defining new style parameters and creative possibilities. A program of design can be thought of as a series of goal-directed decisions / actions out of which an artifact is formed. In the truest sense, the essential "design" of an artifact (e.g., automobile, poster, computer program) is not so much an aspect of its formal or functional attributes as it is a program of decisions/ actions that yielded the artifact. Design is, by definition, nonrandom. Yet, ironically, what this paper will show is that randomness, when interfaced with rules controlling parameters of shape, transformation, and spatial organization, can offer a powerful point of departure in the early stages of the design process. The theoretical context of a "design apprentice," the potential
uses of the technology, and the change it implies from the status quo will also be discussed. DESIGN AND RANDOMNESS
Designing is one of the more complex and strongly goal-directed human cognitive activities[4]. However, since design problems tend to exhibit poorly specified initial conditions, allowable operations and goals, they are among the most ill-defined of problems[ 5 ]. As a subset of general human problem solving, designproblem solving is a process of clarifying the problem, searching for information relevant to its solution, and formulating an acceptable solution within a defined context or problem space[6]. The unifying aspect to the various disciplines wherein design plays a central role (e.g., art, product design, architecture, graphic design, composition of all types, etc.) is the pursuit of novel and functional form. Even so, the defining criteria of "functionality" can vary considerably from discipline to discipline. A limited definition of the designed artifact, but one adequate for the present discussion, suggests a vocabulary of shapes occurring in a field and a set of spatial relations between those shapes [ 7 ] --shape being defined as a finite collection of lines, vocabulary being a set of unique shapes, and spatial relation being a collection or arrangement of shapes. The context of this discussion is roughly limited to design aesthetics: the consideration of beauty and novelty of shape, form, and composition. Functional aspects of design beyond these are not necessarily beyond the scope of the methods presented, but will have to be addressed on another occasion. Randomness is commonly regarded as uninteresting and of little use. My assertion, however, is that something as uninspiring as a table of random numbers found in the appendix of any statistics book can be the source for virtually unlimited graphical richness and usefulness. Fig. 1 shows a computer-generated random array based on 10,000 "coin tosses." The motif or shape indicated at bottom-left was inserted and rotated 45 degrees with each occurrence of heads; the probability is ½ and the distribution is largely homogeneous or even. Despite the simplicity of the generating algorithm, the complexity (information content) of the array exceeds that found in the intricate patterns of Truchet [ 8 ] or even the aperiodic tilings of Penrose [ 9 ]. Interestingly, the local areas of the array are far from
511
512
MICHAEL
ECKERSLEY
S
/ / m
m
/ Fig. 3. Random distribution with linear shape motif (probability l ). This array is generated from the same series of random numbers as in Fig. l; the only difference being the use of a linear motif.
h o m o g e n e o u s (Fig. 2). G i v e n a n infinitely squared b o u n d a r y , the array would contain a virtually infinite diversity o f negative shapes or voids ( " n e g a m o r p h s " ) . T h e array can be described as globally stable but locally
unpredictable, asymmetrical, a n d visually dynamic. Fig. 3 shows the clear topological differences yielded from the same "coin-toss" algorithm using a different motif. O n e can easily scan a r a n d o m array a n d ascribe to isolated areas a n aesthetic value (see Fig. 4). To summarize, by defining one or m o r e shape rules a n d linking the r a n d o m occurrence of particular n u m bers to the invocation o f a few predefined shapes, one can reveal as m a n y u n i q u e structural " c o m p o s i t i o n s "
Fig. 2. Detail section of Fig. 1. While the array is not fractal, close-up views reveal a plethora of graphically complex white and black shapes. Given an infinitely squared boundary, such shapes are virtually unlimited.
Fig. 4. Detail section of another random distribution (probability ½). This array is generated from the same series of random numbers as in Fig. I. Here a triangular motif was inserted and rotated. Compositions similar to this can be found in virtually any foundation level two-dimensional design course. It shows some rather interesting areas of figure-ground reversal, and can be ascribed aesthetic qualities.
Fig. 1. Distribution of shape motifs generated from 10,000 random numbers (probability l ). The motif indicated at bottom-left was inserted and rotated 45 degrees with each occurrence of heads.
Computer-based design apprentice as there are algorithms, shapes, degrees of randomness, and methods of graphical analysis. Paradoxically, such compositions need not, and often do not, appear random. This is likely due to the misconceptions most people have about randomness, and the fact that randomness often seems to "behave" nonrandomly[10, 11]. Waddington [ 10 ] expressed the principle of random variation in biological systems, asserting that organisms turn randomness into order by virtue of their own principles of dynamic organization. Thus the source of heritable variation can be traced not only to random factors, but also to the inherent properties of the generative system[13]. The binding of randomness through rules or controlling parameters yields stochastic structures that are no longer random in the truest sense (if any pure manifestation of randomness is visualizable ), but that maintain an ingredient or imprint of randomness. Thus randomness is naturally creative when it impacts upon systems, causing great diversity based on a common "system theme" (e.g., snowflakes, facial characteristics). Analogous system constraints account for the family of rather exotic aggregate negative shapes in Figs. 1 and 2. Further and more elaborate extensions of the principle can be found in writings on cellular automata theory[14, 15 ], and the Game of "Life" [ 16 ]. Some design theorists have labored to devise deterministic problem solving models with the goal of factoring out chance and variation in the solution to design problems. These methods are intended to prevent any deviation from the optimal design solution. Unfortunately, these efforts usually prove self-defeating for very simple reasons. In real life the interaction of multiple factors usually transforms the problem before its own solution. Virtually any design problem, no matter how simple or highly constrained, will evoke as many solutions as there are designers, or occasions at which a single designer sets out to solve it. What emerges, however, from the often diverse solutions is a common set of overriding design concerns and a set of visual / spatial elements (a visual language) based on a universe of forms (a shape vocabulary) that we all know something about. DESIGN MACHINES AND THE POWER OF GRAMMAR
The notion of a design machine, considered by some to be a contradiction in terms, raises important questions about what constitutes design and to what degree, if at all, the process can be automated. The first serious mention of a design machine was made by Negroponte [ 17 ], and later taken up by Stiny and March [18]. The theory, as treated by Stiny and March, tied into a body of research going back to the 1950s involving formal grammars. The concept of grammar had been used by philosophers and linguists to describe the governing rules or structure of language. Chomsky [ 19 ] showed the possibility of describing and operationalizing grammar by the use of computers. By 1964 the success achieved from such techniques in linguistics led Kirsch [ 20 ] to apply the principle to shapes
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found in pictures and designs. Stiny and Mitchell [ 21 ], Gips [ 22 ], Lauzzana and Pocock-Williams [ 23 ], and others have since popularized and elaborated on research involving shape grammar and applied it to the analysis of architectural design. Most notably Koenig and Eizenberg [ 24 ] conducted an elaborate analysis of what constitutes "style" in the architecture of Frank Lloyd Wright. Koenig and Eizenberg developed a shape grammar for a sample of "prairie-style" houses designed by Wright, and from this grammar they were able to generate three new designs based on the grammar. This most impressive application of shape grammar theory to date has strong implications for the critical and historical study of Wright's architecture, and, for that matter, of architectural design in general. The application of shape grammar theory by Kirsch and Kirsch[25] is of a similar vein; they set out to identify the underlying formal characteristics of a series of modern nonobjective paintings by Richard Diebenkorn. From these characteristics Kirsch and Kirsch inferred a set of rules to account for the common features in the series. Once the features were identified, the task was then to determine the appropriateness and sufficiency of the grammar by (a) demonstrating that any of Diebenkorn's original works from the series could be recreated using the grammar, and (b) generating a series of original compositions based on the grammar and then checking for consistency with the original set of Diebenkorns. Essentially, what each of these production systems amounts to is a kind of design machine consisting of a narrowly constrained knowledge-base and a generator capable of instantiating the rules of the grammar. The strength of these systems lies in their ability to "flesh out" the set of graphic configurations from the possibilities extended by the grammar. Their built-in limitation, on the other hand, is the fixedness of their database. The tools were never intended for creative experimentation in the hands of artists and designers. Imagine, however, if such technology were developed for creative experimentation. Imagine further such tools in the hands of a young Wright or Diebenkorn still actively formulating a personal grammar, still searching for ways to reinvent design. A technology that provides the ability to scan dozens or hundreds of possible outcomes to a given design problem could be intimidating, but it could also be informative in ways traditionally unimaginable. One might make the case that a great mind is facile enough to visualize the vast number of possible configurations extending from a grammar. Unfortunately, most of us are not so blessed. We can assume, however, that the informed novice and professional are quite capable of identifying promising configurations from less-promising ones. Therefore, the goal seems to be one of making valid production systems accessible to these individuals so they might more effectively do what they do best: identify and develop the set of really good design options from the universe of all possible design options. If the creative individual is, by definition, both fluent and flexible in a problem solving context, a pro-
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duction system that can show the breadth and depth of design options would, almost certainly, be an asset to that individual's creative potential. THE DESIGN APPRENTICE
Work by the author on a rule-based interactive design production system varies considerably in scope and intent from those production systems highlighted above, and it rests on the following assumptions: I. Practical shape production systems can be developed using existing relational graphics software; these systems can effectively function as moderately "intelligent" design tools. 2. From an aesthetic context, a designer is only as good as the sophistication, versatility, and novelty of his /her own shape grammar. Use of an interactive shape production system is likely to heighten a designer's awareness of, and experimentation with, style parameters and meta problem solving principles. 3. Randomly configured shapes in a given format space often provide a better point of entry to a design process than does a "blank canvas." 4. For production systems in design to be both useful and practical, they must be open-ended in order to provide as many options as possible to the designer. It is the designer's task to define system parameters, however broad or narrow, even if they conflict with established precedents. Called "Design Apprentice," the production system was written in Apple's Hypercard environment, and it demonstrates the potential for creating powerful and easy-to-use design tools. The purpose of Apprentice is to be a tool for design experimentation and creative discovery. For this reason its control structure and graphics capabilities differ considerably from other shape productions systems; its processes are far less linear and its grammar is variable and less explicit. Design Apprentice is loosely based on the masterapprentice model. As such, its goal attributes are to: • be easily accessible to the designer via a common language • represent the master's design-related beliefs and values • follow instructions implicitly • be a sounding-board for ideas • accumulate and retrieve relevant information • work independently to experiment with and test new visual ideas • exhaust all plausible design possibilities extending from a grammar • work hard and fast • be fun to work with. While preliminary versions of Apprentice don't measure up to some of these desired qualities, with development in the hands of a designer, a surprising number of goal qualities are attainable. In this respect the de-
signer's responsibilities are to teach freely and develop the production system, to articulate as clearly as possible aspects of what he/she knows, and to remain open to the system's feedback. The emphasis here is on a shape grammar development tool that, through interactive use and tinkering, takes on many of the designer's own embedded preferences--not to clone a designer's grammar but to tease it and, perhaps, feed it. Apprentice enters into this relationship as little more than a blank slate with an innate vocabulary of shape primitives (line, shape, texture, etc.--most of which are packaged within Hypercard itself) and a default architecture for following rules through a series of button-invoked scripts accessing the graphics tools. The relative complexity and breadth of the rules and graphical parameters are defined by the mentor. Execution of the rules within the scripts is rapid, making it easy to see the immediate graphical outcome. Apprentice is a Hypercard "stackware" application that runs on any Macintosh with at least IMB of RAM. It consists of three primary background modules: Tablet, Portfolio, and Help. Navigation within and between modules is done by pressing screen buttons using a mouse (see Goodman's[26] book for an in-depth description of the Hypercard relational environment). Tablet is the business-end of Apprentice where shape rules are invoked by button and modified by accessing and changing button scripts. Buttons in Apprentice access the Macintosh graphics tools and allow the user to customize shape-generating algorithms and define general parameters of line size, shape type and size, type font and size, transparency or opacity, etc.--this with only minor knowledge necessary of the relatively transparent Hypertalk programming language (Fig. 5). These changes become the new default parameters until they are redefined by the user. The user can initiate commands one at a time (with the freedom to undo the most recent command) or automate the process, iterating multiple rules consecutively. Apprentice may also be programmed to generate any number of compositions automatically in the designer's absence, storing each composition in Portfolio for later use, adaptation, a n d / o r disposal. Button scripts in Apprentice will also allow for configurations of two-fold and fourfold symmetry. Importantly, the user has continual manual access to the graphics tools except at times when Apprentice is involved in a loop, at which time the user may break-in and assume control. The user may also designate formally or informally within Apprentice the degree of graphical randomness desired, and vary that value instantly. Figs. 6, 7, 8 and 9, 10, l l show the graphic diversity of designs produced through just two of Apprentice's button scripts. DISCUSSION
Researchers working on computerized production systems in design often adopt the agenda of A.I. scie n t i s t s - t h a t being to extend the concept of machine emulation to its ultimate conclusion by taking the human "out of the loop." Aside from the tantalizing intellectual and computational challenges that this goal
Computer-based design apprentice on mouseDown repeat unt il the moueeCl i¢lt put 50,75, 100,125,150,175,200,225,Z50,275,500,525"T'] into xroords put 15,'i0,65,90,I 15, I'~0,165,190,2'15.2q0' mto ycoords put any item o! txeoords) into XI put any item ol (ycoofdSi into YI put XI & "," & YI into upLeft put kl into X2 put random of 10 into Ipool
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nition, limited and limiting. Computers already inflict a confining influence on designers, and this is often evident in the stiffness of their work. If production systems only function to induce narrow thinking in designers, then such systems are exactly what we d o n ' t need. In the short term, and based on an altogether different set of priorities, production systems like Design
i! lpooh-'~ then add 50 to X2 put YI mtoY2 add 50 to Y2 else d ]pooh-6 then add 100foX2 put YI Into ~2 add 100 toY2 else i[ lpoOl-.5 then add 150 toX2 put YI ,nto Y2 add I.'fO to Y2 e.d d put X2 & ....& Y2 into loRight put random of 2 into Zpool if Zpool-I then choose rectangle tool else if Zpool-2 then choose oval tool end if get line$ize set lineSize to random of ~t drag irom upLelt to IoRight wait I put random o| -t into pool if pool-I then set idled to true set pattern to 12 doHenu transparent
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presents, there is also the worrisome possibility that the commercial production o f " e x p e r t design systems" will be marketed to consumers as the final word in design. If such systems were based on the g r a m m a r of Charles Eames or Michael Manwarring, for instance, consumers might assume mistakenly that they were purchasing the perpetual services of these gentlemen. Thus while attainment of this extended goal will require more time and technical development than most researchers are willing to admit [ 27 ], when such systems are developed they might become the equivalent of M Y C I N or D E N D R A L , but used without the wariness or controls that are a part of expert medical diagnosis. Stand-alone systems with a fixed rule-base (as some design researchers would have us build) are, by deftCAG 14:3/4-J
Figs. 6-8, Designs generated from Apprentice's Option 1 Button Script. Grid parameters and variables of shape, texture, line width, "filledness," and probability were defined intuitively by the author.
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MICHAELECKERSLEY
Figs. 9-10. Designs generated from Apprentice's Option 4 Button Script. Here variables of type font and point size are thrown into the mix, as well as a symmetry variable (believe it or not) and an invert algorithm. These designs were taken from a series of 25 compositions generated in about five minutes time. They were each extracted from a larger compositional field and pasted into Portfolio.
Apprentice could become a constituent part of virtually any computer graphics software, including CAD, functioning to foster novelty and the synthesis of diverse forms. With presently existing software technology, artists and designers with virtually no computer programming experience may be enabled to define shape/action rules intuitively and, in turn, transform random number streams into graphical compositions of great variety and complexity. Compositions generated by Apprentice showing limited design promise can be easily "canned," while compositions possessing
areas of interest can be adapted to create designs of considerable aesthetic sophistication. Candidly speaking, at least two of every ten randomly generated designs are often better than the designs of many trained designers (when constrained by the same design parameters). What does this mean? First, it means that stochastic compositions appear quite different from what most people expect randomness would yield (i.e., such compositions often appear "designed"). Second, few designers and design educators have compared compositions generated sto-
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Fig. 11. Six Designs generated from Apprentice's Option 3 Button Script. Randomized design using twofold reflectivesymmetry as a variable. Some comparison can be drawn between these and similar decorative designs by Frank Lloyd Wright.
Computer-based design apprentice chastically with compositions developed m a n u a l l y by individuals working essentially the same design problem [28 ]. In conclusion, design p r o d u c t i o n systems s h o u l d n ' t be regarded as a threat to the designer, b u t rather as a potentially useful design tool. After all, such systems m a y someday exist as a constructive challenge to students a n d educators alike, saying: " Y o u d o n ' t have to start from scratch with each new d e s i g n - - w h y not just improve incrementally on what r a n d o m n e s s gives you? If you want to push the b o u n d a r i e s of a design style, or switch styles completely, simply work higher up the ladder on the level o f rules a n d probability.
REFERENCES
1. R. A. Kirsch, Computer interpretation of English text and picture patterns. 1EEE Transactions on Electronic Computers 13, 363-367 (1964). 2. H. Koning and J. Eizenberg, The language of the prairie: Frank Lloyd Wright's prairie houses. Environment and Planning B 8, 295-323 ( 1981 ). 3. G. Stiny and W. J. Mitchell, The grammar of paradise: On the generation of Mughul gardens. Environment and Planning B 7, 209-226 (1980). 4. H. A. Simon, The Sciences ~fthe Artificial. MIT Press, Cambridge, MA ( 1971 ). 5. H. A. Simon, The structure of ill-structured problems. A rti[icial Intelligenee 4, 181-201 ( 1973 ). 6. N. Cross, The relevance of cognitive styles in design education. Design .~lethods and Theories 17(I), 37-49 (1983). 7. T.W. Knight, Languages of design: From known to new. Environment and Planning B 8, 213-238 ( 1981 ). 8. C. S. Smith, The tiling patterns of Sebastien Truchet and the topology of structural hierarchy. Leonardo 20(4), 373-385 (1987). 9. B. Grunbaum and G. C. Shephard, Tilings and Patterns, W. H. Freeman and Co., New York (1986). 10. R. Falk, The perception of randomness. Paper presented at the Fi[?h Conference of the International Group .for Psychology of Mathematical Education, Grenoble, France (1981).
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11. K. H. Teigen, Studies in subjective probability I: prediction of random events. Scandanavian Journal of Psychology 24, 13-25 (1983). 12. C. H. Waddington, The Strategy of the Genes, George Allen and Unwin, London (1957). 13. P. T. Saunders, The evolution of form and pattern. Leonardo 22( 1), 38-39 (1989). 14. J. von Neuman, Theory of SelJ:Reproducing Automata. (Text was authored by Wolfram, edited and completed by A. W. Burks.) University of lllinois Press, Urbana (1966). 15. S. Wolfram, Theory and Applications ~f Cellular Automata, World Scientific Publishing Co. Pte Ltd., Singapore (1986). 16. E. R. Berlekamp, J. H. Conway and R. K. Guy, Winning Ways. Vol. 2, Chapter 25, Academic Press, London (1982). 17. N. Negroponte, The Architecture Machine, MIT Press, Cambridge, MA (1970). 18. G. Stiny and L. March, Design machines. Environment and Planning B 8, 245-255 ( 1981 ). 19. N. Chomsky, Aspeets of the Theory ~] Svntax, MIT Press, Cambridge, MA (1965). 20. R. A. Kitsch, Computer interpretation of English text and picture patterns. IEEE Transactions on Electronic Computers 13, 363-376 (1964). 21. G. Stiny and W. J. Mitchell, The grammar of paradise: On the generation of Mughul gardens. Environment and Planning B 7, 209-226 (1980). 22. J. Gips, Shape Grammars and Their Uses, Birkhauser, Basel (1975). 23. R. G. Lauzzana and L. Pocock-Williams, A rule system for analysis in the visual arts. Leonardo 21 (4), 445-452 (1988). 24. H. Koenig and J. Eizenberg, The language of the prairie: Frank Lloyd Wright's prairie houses. Environment and Planning B 8, 295-323 ( 1981 ). 25. J. L. Kirsch and R. A. Kirsch, The structure of paintings: Formal grammar and design. Environment and Planning B 13, 163-176 (1986). 26. D. Goodman, The Complete Hypercard Handbook, Bantam Books, Toronto (1988). 27. M. Eckersley, On heuristics and design-problem solving behavior: Cracking the "black box." Design Methods and Theories 24(3) (1990). 28. M. Eckersley, Rules, randomness and compositional structure in design. Leonardo 23 ( 1 ) ( 1990 ).