Creativity, emergence and evolution in design
Gero, J.S.: Creativity, emergence and evolution in design. Knowledge-Based Systems 9(7), 435–448 (1996)
This paper commences by outlining notions of creativity before examining the role of emergence in creative design. Various process models of emergence are presented; these are based on notions of additive and substitutive variables resulting in additive and substitutive schemas. Frameworks for both representation and process for a computational model of creative design are presented. The representational framework is based on design prototypes whilst the process framework is based on an evolutionary model. The computational model brings both representation and process together.
 G. Broadbent, Design in Architecture, Wiley, London, 1973.
 P.G. Rowe, Design Thinking, MIT, Cambridge, 1987.
 R.D. Coyne, M.A. Rosenman, A D . Radford, M. Balachandran and J.S. Gero, Knowledge-Based Design Systems. Addison- Wesley, Reading, 1990.
 B. Lawson, How Designers Think, Butterworths, London, 1990.
 T. Smithers, Design as exploration: puzzle-making and puzzle- solving, AID92 Workshop on Search-Based and Exploration- Based Models of Design Process (available from the Department of Artificial Intelligence, Edinburgh University), 1992, pp. 1 21.
 M. Boden, The Creative Mind, Myths and Mechanisms, Wieden- feld and Nicholson, London, 1991.
 J.S. Gero and M,L. Maher, Mutation and analogy to support creativity in computer-aided design, in G.N. Schmitt (ed.), CAAD Futures "91, Vieweg, Wiesbaden, 1992, pp. 261 270.
 J.S. Gero and M.L. Maher, (eds.) Modeling Creativity and Knowledge-Based Creative Design, Lawrence Erlbaum, Hillsdale, N J, 1993.
 S.H. Kim, Essence of" Creativity. Ox[\~rd University Press, New York. 1990.
 R. Sternberg, (ed.) The Nature of Creativit}, Cambridge Univer- sity Press, Cambridge, 1988.
 R.W. Weisberg, Creativity: Genius and Other Myths, W.H. Free- man, New York, 1986.
 J.S. Gero, Design prototypes: a knowledge representation schema for design, AI Mag., 11 119901 26 36.
 J. de Kleer and J.S. Brown, Qualitative physics based on con- fluences, Artif. Intell., 24 (19841 7 83.
 R. Ganesham, S. Finger and J. Garrett, Representing and reason- ing with design intent, in J.S. Gero (ed.), Artificial Intelligence in Design "91. Butterworth-Heinemann, 1991, pp. 737 755.
 A.C.B. Garcia and C. Howard, Building a model for augmented documentation, in J.S. Gero (ed.). Artificial Intelligence in Design "91, Butterworth-Heinemann, 1991, pp. 723 736.
 M.A. Rosenman, Incorporating intent in design data exchange standards, in J.S. Gero and F. Sudweeks (eds.). Preprints IJCAI- 91 Workshop on Artificial Intelligence in Design, University of Sydney, Sydney, Australia, 1991, pp. 51 56.
 M.A. Rosenman and J.S. Gero, Modelling multiple views of design objects in a collaborative CAD environment, Comput.- Aided Des., 28 (1996) 193 2115.
 J. Beattie, An essay on laughter and ludicrous composition, Essays, William Creech, Edinburgh, 1776.
 A. Koestler, The Act of Creation, Hutchinson, London, 1964.
 J.M. Suls, A two-stage model for the appreciation of jokes and cartoons, in J. Goldstein and P. McGhee (eds.), Psychology of Humor, Academic Press, New York, 1972.
 S.S. Stevens, On the psychophysical laa. Psychol. Rev., 64 ( 19571 153 181.
 J.S. Gero and B. Kumar, Expanding design spaces through new design variables. Des. Stud., 14 (19931 210 221.
 J.S. Gero, S. Serino and D. Choi, A computational model of emergent visuai forms, Cog. Sci., 93 119931 85 87.
 R. Finke, Creative Imagery, Lawrence Erlbaum, Hillsdale, N J, 1991/.  N.Y. Foo and B.P. Ziegler, Emergence and computation. Int. J. Gen. Syst., 10 (19851 163 168.
 S. Forrest, (ed.) Emergent Computation, Elsevier, New York, 1990.
 K.R. Koedinger, Emergent properties and structural constraints: advantages of diagrammatic representations for reasoning and learning, in N H . Narayanam (ed.), Workshop Notes AAAI Spring Symposium on Reasoning with Diagrammatic Representa- tions, Stanford University, Palo Alto, 1992, pp. 154 149.
 J.S. Gero and M. Yan, Shape emergence using symbolic reason- ing. Environ. Plann. B: Plann. Des.. 21 119941 191 212.
 J.S. Gero and J, Damski, Object emergence in 3D using a data driven approach, in J.S. Gero and F. Sudweeks (eds.) Artificial Intelligence in Design '94, Kluwer, Dordrecht, 1994, pp. 419 436.
 Y-T. Liu, Encoding explicit and implicit emergent subshapes based on empirical findings about human vision, in J.S. Gero and F. Sudweeks (eds.), Artificial Intelligence in Design "94, Kluwer, Dordrecht, 1994, pp. 401-418.
 D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA, 1989.
 J.G. Carbonell, Learning by analogy: formulating and generalis- ing plans from past experience, in R.S. Michalski, J.G. Carbonell and T.M. Mitchell (eds.), Machine Learning: An Artificial Intelli- gence Approach, Tioga, Palo Alto, CA, 1983, pp. 137-161.
 J.G. Carbonell, Derivational analogy: a theory of reconstructive problem solving and expertise acquisition, in R.S. Michalski, J.G. Carbonell and T.M. Mitchell (eds.), Machine Learning II: An Artificial Intelligence Approach, Morgan Kaufmann, Los Altos, CA, 1986, pp. 371-392.
 L. Qian and J.S. Gero, A design support system using analogy, in J.S. Gero (ed,), Artificial Intelligence in Design '92, Kluwer, Dordrecht, 1992, pp. 795 816.
 J.H. Jo and J.S. Gero, Design mutation as a computational process, in G. Woodbury (ed.), The Technology of Design, ANZAScA, University of Adelaide, Adelaide, 1991, pp. 135 143.
 J.S. Gero, S. Louis and S. Kundu, Evolutionary learning of novel grammars for design improvement, AIEDAM, 8 (1994) 83 94.
 A.T. Purcell and J.S. Gero, The effects of examples on the results of a design activity, in J.S. Gero (ed.), Artificial Intelligence in Design '91, Butterworth-Heinemann, Oxford, 1991, pp. 525 542.
 J. Damski and J.S. Gero, Visual reasoning as visual re-interpreta- tion through re-representation, AID '94 Workshop on Reasoning with Shapes in Design, Lausanne, 1994, pp. 16 20.
 R.F. Woodbury, Design genes, Preprints Modeling Creativity and Knowledge-Based Creative Design, Design Computing Unit, Department of Architectural and Design Science, University of Sydney, Sydney, 1989, pp. 133 154. (appeared as A genetic approach to creative design in J.S. Gero and M.L. Maher (eds.) Modeling Creativity and Knowledge-Based Creative Design, Lawrence Erlbaum, Hillsdale, N J, pp. 211 232.).
 P. Steadman, The Evolution of Designs, Cambridge University Press, Cambridge, 1979.
 I. Hybs and J.S. Gero, An evolutionary process model of design. Des. Stud., 13 (1992) 273 290.
 S. Louis and G.J. Rawlings, Designer genetic algorithms: genetic algorithms in structure design, in R.K. Belew and L.B. Booker (eds.), Proc. Fourth Int. Conf. on Genetic Algorithms, Morgan Kaufmann, San Mateo, 1991, pp. 53-60.
 J. Holland, Adaptation in Natural and Artificial Systems, Univer- sity of Michigan Press, Ann Arbor, 1975,
 J.S. Gero and V. Kazakov, An exploration-based evolutionary model of a generative design process, Microcomput. Civ. Eng., 11 (1996) 209-216.
 I. Harvey, The artificial evolution of behaviour, in J.-A. Meyer and S.W. Wilson (eds.), From Animals to Animats, MIT Press, Cambridge, 1991, pp. 400-408.
 J.S. Gero and V. Kazakov, Evolving building blocks for design using genetic engineering: a formal approach, in J.S. Gero (ed.), Advances in formal design methods for CAD, Chapman and Hall, London, 1996, pp. 31 50.
 G.L. Langton, Artificial Life, Addison-Wesley, Reading, 1989.
 L. Steels, Towards a theory of emergent functionality, in J.-A. Meyer and S.W. Wilson (eds.), From Animals to Animats, MIT Press, Cambridge, 1991, pp. 451-461.