Curious Design Agents and Artificial Creativity
Inhaltsverzeichnis
Reference
Saunders, R.: Curious Design Agents and Artificial Creativity. Ph.D. thesis, University of Sydney, Sydney, Australia (2001).
DOI
Abstract
Creative products are generally recognised as satisfying two requirements: firstly they are useful, and secondly they are novel. Much effort in AI and design computing has been put into developing systems that can recognise the usefulness of the products that they generate. In contrast, the work presented in this thesis has concentrated on developing computational systems that are able to recognise the novelty of their work. The research has shown that when computational systems are given the ability to recognise both the novelty and the usefulness of their products they gain a level of autonomy that opens up new possibilities for the study of creative behaviour in single agents and the emergence of social creativity in multi-agent systems. The work presented in this thesis has developed a model of curiosity in design as the selection of design actions with the goal of generating novel artefacts. Agents that embody this model of curiosity are called “curious design agents”. The behaviour of curious design agents is demonstrated with a range of applications to visual and non- visual design domains. Visual domains include rectilinear drawings, Spirograph patterns, and “genetic artworks” similar to the work of Karl Sims. Non-visual domains include an illustrative abstract design space useful for visualising the behaviour of curious agents and the design of doorways to accommodate the passage of large crowds. The design methods used in the different domains show that the model of curiosity is applicable to models of designing by direct manipulation, parametric configuration or by using a separate design tool that embodies the generative aspects of the design process. In addition, an approach to developing multi-agent systems with autonomous notions of creativity called artificial creativity is presented. The opportunities for studying social creativity in design are illustrated with an artificial creativity system used to study the emergence of social notions of whom and what are creative in a society of curious design agents. Developing similar artificial creativity systems promises to be a useful synthetic approach to the study of socially situated, creative design.
Extended Abstract
Bibtex
Used References
Aloimonos, J., Weiss, I. and Bandyopadhyay, A. (1988) Active vision, International Journal of Computer Vision, 1(4): 333–356.
Amabile, T. M. (1983) The Social Psychology of Creativity, Springer-Verlag, New York.
Amabile, T. M. (1985) Motivation and creativity: Effects of motivational orientation on creative writing, Journal of Personality and Social Psychology, 48: 393–399.
Baker, E. (1998) The Mug-Shot Search Problem:A Study of the Eigenface Metric, Search Strategies, and Interfaces in a System for Searching Facial Image Data, Ph.D. Thesis, Harvard University.
Baker, E. and Seltzer, E. (1994) Evolving line drawings, in W. Davis and B. Joe (eds.) Proceedings of Graphic Interface ’94, Morgan Kaufmann Publishers, pp. 91–100.
Baker, E. and Seltzer, E. (1998) The mug-shot search problem, Proceedings of Vision Interface ’98, Canada.
Baker, D. L., Hofmann, T., McCallum, A. K. and Yang, Y. (1999) A hierarchical probabilistic model for novelty detection in text, Technical report, Just Research. Baluja, S. (1996) Expectation-Based Selective Attention, Ph.D. Thesis, Carnegie Mellon University.
Baluja, S., Pomerleau, D. A. and Jochem, T. (1994) Towards automated artificial evolution for computer generated images, Connection Science 6(2&3): 325–354.
Baraldi, A. and Alpaydin, E. (1998) Simplified ART: A new class of ART algorithms, International Computer Science Institute, Berkeley, CA, TR-98-004.
Bartfai, G. (1994) Hierarchical clustering with ART neural networks, Technical Report CS-TR-94/1, Department of Computer Science, Victoria University of Wellington, New Zealand.
Bentley, P. J. (1999a). Evolutionary Design by Computers. Morgan Kaufman, San Francisco, CA.
Bentley, P. J. (1999b). An introduction to evolutionary design by computers, in Bentley, P. J. (ed.). Evolutionary Design by Computers. Morgan Kaufman, San Francisco, CA, pp. 1–73.
Bentley, P.J. (1999c) From Coffee Tables to Hospitals: Generic Evolutionary Design, in Bentley, P. J. (ed.). Evolutionary Design by Computers. Morgan Kaufman, San Francisco, CA, pp. 405–423.
Bentley, P. J. (1999d). Is evolution creative? in P. J. Bentley and D. Corne (eds) Proceedings of the AISB'99 Symposium on Creative Evolutionary Systems (CES), The Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB), pp. 28–34.
Bentley, P. J. (2000) Creative Evolution, in David Smith (ed.) UCL Science 14, University College London, pp. 14–15.
Berlyne, D. E. (1960) Conflict, Arousal and Curiosity, McGraw-Hill, New York.
Berlyne, D. E. (1971) Aesthetics and Psychobiology, Appleton-Century-Crofts, New York.
Boden. M. A. (1990) The Creative Mind: Myths and Mechanisms. Cardinal, London.
Boden, M. A. (1999) Computer models of creativity, in Robert J. Sternberg (ed.) The Handbook of Creativity, Cambridge University Press, UK, pp. 351–372.
Bringsjord, S. (2001) Is It Possible to Build Dramatically Compelling Interactive Digital Entertainment? Game Studies, 1.
Broadbent, G. (1973) Design in Architecture : Architecture and the Human Sciences, John Wiley and Sons, London.
Bryant, C. H., Muggleton, S. H., Page, C. D., and Sternberg, M. J. E. (1999) Combining active learning with inductive logic programming to close the loop in machine learning, AISB’99 Symposium on AI and Scientific Creativity, Edinburgh, Scotland, pp. 59–64.
Cagan, J. and Agogino, A. M. (1991) Dimensional variable expansion - a formal approach to innovative design, Research in Engineering Design 3: 75–85.
Carpenter, G., Grossberg, S. (1987a) A massively parallel architecture for a self- organizing neural pattern recognition machine, Computer Vision, Graphics and Image Processing, 37 54–115.
Carpenter, G., Grossberg, S. (1987b) ART2: Self-organization of stable category recognition codes for analog input patterns, Applied Optics 26(23) 4919–4930.
Carpenter, G., Grossberg, S. (1990) ART3: Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures, Neural Networks 3 129-152.
Carpenter, G., Grossberg, S., and Rosen, D. B. (1991a) ART2-A: An adaptive resonance algorithm for rapid category learning and recognition, Neural Networks, 4 493–504.
Carpenter, G., Grossberg, S., and Rosen, D. B. (1991b) ARTMAP: Supervised real- time learning and classification of nonstationary data by a self-organizing neural network, Neural Networks, 4 565–588.
Carpenter, G., Grossberg, S., and Rosen, D. B. (1991c) Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system, Neural Networks, 4 759–771.
Carpenter, G., Grossberg, S., Markuzon, N., Reynolds, J. H., Rosen, D. B. (1992) Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps, IEEE Transactions on Neural Networks, 3(5) 698–713.
Cha, M. Y. and Gero, J. S. (1998) Shape pattern recognition using a computable shape pattern representation, in J. S. Gero and F. Sudweeks (eds), Artificial Intelligence in Design '98, Kluwer, Dordrecht, pp. 169–188.
Clancey, W. J. (1997) Situated Cognition: On Human Knowledge and Computer Representations, Cambridge University Press, Cambridge, England.
Clark, A. (1994) Connectionism and cognitive flexibility, in T. Dartnall (ed.) Artificial Intelligence and Creativity, Kluwer Academic Publishers, Netherlands, pp. 63–79.
Coates, P. (1997) Using genetic programming and L-systems to explore 3D design worlds, in R. Junge (ed.), CAADFutures '97, Kluwer Academic Publishers, Netherlands.
Cohen, H. (1999) AI and Visual Creativity, AISB ’99 Convention Proceedings, AISB, Edinburgh.
Collins, M. A. and Amabile, T. M. (1999) Motivation and creativity, in Robert J. Sternberg (ed.) The Handbook of Creativity, Cambridge University Press, UK, pp. 297–312.
Colton, S., Bundy, A. and Walsh, T. (2000a) On the notion of interestingness in automated mathematical discovery, International Journal of Human Computer Studies, 53(3).
Colton, S., Bundy, A. and Walsh, T. (2000b) Agent Based Cooperative Theory Formation in Pure Mathematics, in G. Wiggins (ed.), Proceedings of AISB 2000 Symposium on Creative and Cultural Aspects and Applications of AI and Cognitive Science, Birmingham, UK, pp. 11–18.
Colton, S. and Steel, G. (1999) Artificial intelligence and scientific creativity, Journal of the Society for Artificial Intelligence and the Study of Behaviour, 102: 44–50.
Crevier, D. (1993) AI: The Tumultuous History of the Search for Artificial Intelligence, MIT Press, Cambridge, MA.
Csikszentmihalyi, M. (1988) Society, culture, and person: a systems view of creativity, in R. J. Sternberg (ed.), The Nature of Creativity, Cambridge University Press, Cambridge, UK, pp. 325–339.
Csikszentmihalyi. M. (1996a) Creativity: Flow and the Psychology of Discovery and Invention, Harper Collins, New York.
Csikszentmihalyi, M. (1996b) The creative personality, Psychology Today, Ziff- Davis, New York, July/August: 36–40.
Csikszentmihalyi, M. (1999) Implications of a Systems Perspective for the Study of Creativity, in R. J. Sternberg (ed.), Handbook of Creativity, Cambridge University Press, Cambridge, UK, pp. 313–335.
Dacey, J. S. and Lennon, K. H. (1998) Understanding Creativity, Jossey-Bass, San Fransisco.
Damski, J. C. and Gero J. S., (1996) A logic-based framework for shape representation, Computer-Aided Design, 28(3): 169-181.
Davis, R. and Lenat D. (1982) Knowledge-Based Systems in AI, McGraw-Hill Advanced Computer Science Series.
Dawkins, R. (1987) The Blind Watchmaker, Norton and Co., London.
Dawkins, R. (1989) The evolution of evolvability, in C. G. Langton (ed.) Artificial Life, Addison–Wesley, Reading, MA, pp. 201–220.
de Bono, E. (1986) Lateral Thinking, Harper and Row, New York.
Dewey, J. (1910) How We Think, Heath, London.
Eby, D., Averill, R., Goodman, E. and Punch, W. (1999) The optimization of flywheels using an Injection Island Genetic Algorithm, in P. Bentley, (ed.), Evolutionary Design by Computers, Morgan Kaufmann, San Francisco, pp. 167– 190.
Edmonds, E. and Soufi, B. (1992) The computational modelling of emergent shapes in design, in Gero, J. S. and Sudweeks, F. (eds), Computational Models of Creative Design, The University of Sydney, pp 173–189.
Einstein, A. and Infeld, L. (1938) The Evolution of Physics, Simon and Schuster Press, New York.
Elman, J. (1990) Finding structure in time, Cognitive Science, 14: 179-211.
Elton, M. (1995) Artificial creativity: Enculturing computers, Leonardo, 28(3): 207– 213
Finke, R. A., Ward, T. B. and Smith, S. M. (1992) Creative Cognition: Theory, Research and Applications, MIT Press, Cambridge, MA.
Franklin, S. (1997a) Artificial Minds, MIT Press, Cambridge, MA.
Franklin, S. (1997b) Autonomous agents as embodied AI, Cybernetics and Systems, 28:6 499–520.
Franklin, S. and Graesser, A. (1996) Is it an Agent, or just a Program?: A taxonomy for autonomous agents, in J. P. Mueller, M. J. Wooldridge, and N. R. Jennings (eds), Intelligent Agents III, Springer-Verlag, Berlin, pp. 21–36.
Fritzke, B. (1996) Growing self-organizing networks – Why? in M. Verleysen (ed.) ESANN’96: European Symposium on Artificial Neural Networks, D-Facto Publishers, Brussels, pp. 61–72.
Gabora, L. (1995) Meme and variations: A computer model of cultural evolution, in L. Nadel & D. Stein, (eds) 1993 Lectures in Complex Systems, Addison-Wesley, Reading.
Gabora, L. (1996) Culture, evolution, and computation, in T. Furuhashi, (ed.) Proceedings of the Second Online Workshop on Evolutionary Computation, Society of Fuzzy Theory and Systems, pp. 9–12.
Gabora, L. (1997) The origin and evolution of culture and creativity. Journal of Memetics: Evolutionary Models of Information Transmission 1(1): 1–28.
Gabora, L. (2000) The beer can theory of creativity, in P. Bentley & D. Corne, (eds) Creative Evolutionary Systems, Morgan Kauffman, San Francisco, CA, pp. 1–26. Gardner, H. (1993) Creating Minds, Basic Books, New York.
Gaver, W. W. and Mandler, G. (1987) Play it again, Sam: On liking music, Cognition and Emotion, 1(3): 259–282.
Gero, J. S. (1994b) Computational models of creative design processes, in T. Dartnall (ed.) Artificial Intelligence and Creativity, Kluwer Academic Publishers, Netherlands, pp. 269–281.
Gero, J. S. and Jun, H. J., (1995) Visual semantics emergence to support creative designing: a computational view, in Gero, J. S., Maher, M. L. and Sudweeks, F. (eds), Preprints Computational Models of Creative Design, Key Centre of Design Computing, Department of Architectural and Design Science, The University of Sydney, Sydney, pp. 87–116
Gero, J. S. and Saunders, R. (2000) Constructed representations and their functions in computational models of designing, in B–K. Tang, M. Tan and Y–C. Wong (eds), Proceedings of the Fifth Conference on Computer Aided Architectural Design Research in Asia (CAADRIA 2000), CASA, Singapore, pp. 215–224.
Gero, J. S. and Schnier, T. (1995). Evolving representations of design cases and their use in creative design, in J. S. Gero, M. L. Maher and F. Sudweeks (eds), Preprints Computational Models of Creative Design, Key Centre of Design Computing, University of Sydney, pp. 343–368.
Gero, J. S. and Yan, M., (1993) Discovering emergent shapes using a data-driven symbolic model, in Flemming, U. and Van Wyk, S. (eds), CAAD Futures '93, North Holland, Amsterdam, pp. 3–17.
Goertzel, B. (1997) From Complexity to Creativity, Plenum Press, New York.
Goldberg, D. E. (1999) The race, the hurdle and the sweet spot: Lessons from genetic algorithms for the automation of design innovation and creativity, in Bentley, P. J. (ed.) Evolutionary Design by Computers, Morgan Kaufman, San Francisco, CA, pp. 105–118.
Gomes, P., Bento C., and Gago O. (1999) A function for novelty evaluation in design, in J. S. Gero and M. L. Maher (eds) Computational Models of Creative Design IV, Key Centre of Design Computing and Cognition, University of Sydney, pp. 19– 31.
Graf, J. and Banzhaf, W. (1995) Interactive evolution of images, in J. R. McDonnell, R. G. Reynolds, and D. B. Fogel (eds) Evolutionary Programming IV: Proceedings of the Fourth Annual Conference on Evolutionary Programming.
Grossberg, S. (1976) Adaptive pattern classication and universal recoding, II: Feedback, expectation, olfaction and illusions, Biological Cybernetics 23: 187– 202.
Grossberg, S. and Mingolla, E. (1985a) Neural dynamics of form perception: Boundary completion, illusory figures, and neon color spreading, Psychological Review, 92: 173–211.
Grossberg, S. and Mingolla, E. (1985b) Neural dynamics of perceptual grouping: Textures, boundaries, and emergent segmentation, Perception & Psychophysics, 38: 141–171.
Guilford, J. P. (1967) The Nature of Human Intelligence, McGraw-Hill, New York.
Haag, G. and Liedl, P. (2001) Modelling and simulating innovation behaviour within micro-based correlated decision processes, Journal of Artificial Societies and Social Simulation, 4(3), http://www.soc.surrey.ac.uk/JASSS/4/3/3.html.
Hayes-Roth, F. (1983) Using proofs and refutations to learn from experience, Machine Learning: An Artificial Intelligence Approach, Springer-Verlag, Berlin, pp. 221–240.
Helbing, D. (1991) A mathematical model for the behavior of pedestrians Behavioral Science, 36: 298–310.
Helbing, D. and Molnár, P. (1995) Social force model for pedestrian dynamics, Physical Review E, 51: 4282–4286.
Helbing, D. and Molnár, P. (1997) Self-organization phenomena in pedestrian crowds, in F. Schweitzer (ed.) Self-Organization of Complex Structures: From Individual to Collective Dynamics, Gordon and Breach, London, pp. 569–577.
Hofstadter, D. (1979) Godel, Escher, Bach: An Eternal Golden Braid, Basic Books, New York.
Hofstadter, D. and the Fluid Analogies Research Group (1995a) Fluid Concepts & Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought, Basic Books, New York.
Hofstadter, D. (1995b) Creativity, brain mechanisms, and the Turing Test, in D. Hofstadter (ed.) Fluid Concepts & Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought, Basic Books, New York, pp. 467–491.
Home, H. (1765) Elements of Criticism, Millar, London.
Humphrey, N. K. (1973) The illusion of beauty, Perception, 2: 429–439.
Kaski, S., Kangas, J. and Kohonen, T. (1998) Bibliography of self-organizing map (SOM) papers: 1981-1997, Neural Computing Surveys, 1(3&4): 1–176.
Koestler, A. (1964) The Act of Creation, Macmillan, New York.
Kohonen, T. (1993) Self-Organization and Associative Memory, 3rd ed., Springer, Berlin. Kohonen, T. (1995) Self-Organizing Maps, Springer–Verlag, Berlin.
Koning, H. and Eizenberg, J. (1981) The language of the prairies: Frank Lloyd Wright's Prairie Houses, Environment and Planning B, 8: 295–323.
Koza, J. R. (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA.
Koza, J. R., Bennett III, F. H., Andre, D., and Keane, M. A. (1999) Genetic Programming III: Darwinian Invention and Problem Solving, Morgan Kaufmann, San Francisco, CA.
Langley, P., Simon, H. A., Bradshaw, G. L., and Zytkow, J. M. (1987) Scientific Discovery: Computational Explorations of the Creative Processes, MIT Press, Cambridge, MA.
Langton, C. G. (1989) Artificial life, Artificial Life, Addison–Wesley, Reading, pp. 1– 47.
Lenat, D. B. (1976) AM: An artificial intelligence approach to discovery in mathematics, Ph.D. thesis, Stanford University.
Lenat, D. B. (1983) EURISKO: A program that learns new heuristics and domain concepts, Artificial Intelligence, 21:61–98.
Lenat, D. B. and Brown, J. S. (1984) Why AM and EURISKO appear to work, Artificial Intelligence, 23:269–294.
Lewis, M. (2000) Aesthetic evolutionary design with data flow networks, Generative Art 2000, Milan.
Lipson, H. and Pollack, J. B. (2000) Automatic design and manufacture of artificial lifeforms, Nature, 406: 974–978.
Lindsay, R., Buchanan, B., Feigenbaum, E., and Lederberg, J. (1993) DENDRAL: A case study of the first expert system for scientific hypothesis formation, Artificial Intelligence, 61(2): 209–261.
Liu, Y–T., (1993) A connectionist approach to shape recognition and transformation, in Flemming, U. and Van Wyk, S. (eds), CAAD Futures '93, North Holland, Amsterdam, pp. 19–36.
Liu, Y. T. (2000) Creativity or novelty? Design Studies 21(3): 261–276.
Macedo, L. and Cardosa, A. (2001a) Creativity and surprise, in G. Wiggins (ed.), AISB'01 Symposium on AI and Creativity in Arts and Science, SSAISB, York, UK. Macedo, L. and Cardosa, A. (2001b) SC-EUENE – Surprise/curiosity-based exploration of uncertain and unknown environments, AISB'01 Symposium on Emotion, SSAISB, York, UK.
Maes, P. (1994) Agents that reduce work and information overload, Communications of the ACM, 37(7): 31–40.
Maher, M. L., Gu, N. and Li, F. (2001),Visualisation and object design in virtual architecture, in J. S. Gero, S. Chase and M. Rosenman (eds), CAADRIA2001, Key Centre of Design Computing and Cognition, University of Sydney, pp. 39–50.
Major, M. D., Stonor, T. and Penn, A. (1998) Passengers, pedestrians and shoppers: Space syntax in design, Passenger Terminal World, April.
Mandelbrot, B. B. (1977) The Fractal Geometry of Nature, W. H. Freeman & Co., New York.
Marsland, S., Nehmzow, U. and Shapiro, J. (2000a) Novelty detection for robot neotaxis, International Symposium On Neural Computation (NC'2000), pp. 554– 559.
Marsland, S., Nehmzow, U. and Shapiro, J. (2000b) A real–time novelty detector for a mobile robot, EUREL European Advanced Robotics Systems Conference – Robotics 2000, Salford, England.
116Marsland, S., Nehmzow, U. and Shapiro, J. (2000c) Novelty detection on a mobile robot using habituation, From Animals to Animats, The Sixth International Conference on Simulation of Adaptive Behaviour, Paris, France.
Marsland, S., Nehmzow, U. and Shapiro, J. (2001) Novelty detection in large environments, Proceedings of Towards Intelligent Mobile Robots 2001 (TIMR'01), Bristol, England.
Martindale, C., Moore, K., and West, A. (1988) Relationship of preference judgements to typicality, novelty, and mere exposure, Empirical Studies of the Arts, 6(1).
Martindale, C. (1990) The Clockwork Muse, Basic Books, New York.
Newell, A., Shaw, J. C. and Simon, H. A. (1962) The process of creative thinking, in H. Gruber, G. Terrell and M. Wertheimer (eds), Contemporary Approaches to Creative Thinking, Atherton Press, New York, pp. 63–119.
Nwana, H. (1996) Software agents: An overview. The Knowledge Engineering Review 11(3).
Partridge, D. Rowe, J. (1994) Computers and Creativity, Intellect Books, Oxford. Peters, M. (2000) Making Sense: Autonomy and adaptation in visual robotics, Ph.D. Thesis, The University of New South Wales, Australia.
Pollack, J. B. (1990) Recursive distributed representations, Artificial Intelligence 46(1): 77–105.
Purcell, T. A. and Gero, J. S. (1998) Drawings and the design process, Design Studies 19: 389–430.
Reffat, R. and J. S. Gero (1998) Learning about shape semantics: A situated learning approach, in T. Sasada, S. Yamaguchi, M. Morozumi, A. Kaga and R. Homma (eds), CAADRIA'98, CAADRIA, Kumomoto, Japan, pp. 375–384.
Ritchie, G. D. and Hanna, F. K. (1984) AM: A case study in AI methodology, Artificial Intelligence Journal, 23: 249–263.
Rivest, R. L., Shamir, A., and Adleman, L. (1978) A method for obtaining digital signatures and public key cryptosystems, Communications of the ACM 21(2): 120–126.
Rosenman, M. A. (1996) A growth model for form generation using a hierarchical evolutionary approach, Microcomputers in Civil Engineering, special issue on Evolutionary Systems in Design, 11(3): 161–172.
Rosenman, M. A. and Gero, J. S. (1993) Creativity in design using a design prototype approach, in J. S. Gero and M. L. Maher (eds), Modeling Creativity and Knowledge-based Creative Design, Lawrence Erlbaum, Hillsdale, New Jersey, pp.111–138.
Rowe, J. and Partridge, D. (1993) Creativity: A survey of AI approaches, Artificial Intelligence Review, 7: 43–70.
Schmidhuber, J. (1991a) Adaptive confidence and adaptive curiosity, Technical Report FKI-14991, Technische Universitat München, München, Germany.
Schmidhuber, J. (1991b) A possibility for implementing curiosity and boredom in model-building neural controllers, in J. A. Meyer and S. W. Wilson (eds) Proceedings of the International Conference on Simulation of Adaptive Behaviour: From Animals to Animats, MIT Press, Cambridge, MA. pp. 222–227.
Schmidhuber, J. (1991c) Curious model-building control systems, in Proceedings of the International Joint Conference on Neural Networks, Singapore, IEEE, volume 2, pp. 1458–1463.
Schmidhuber, J. (1997) What’s interesting? Technical Report TR-35-97, IDSIA, Lugano, Switzerland.
Schnier, T. and Gero, J. S. (1996) Learning genetic representations as alternative to hand-coded shape grammars, in J. S. Gero and F. Sudweeks (eds), Artificial Intelligence in Design '96, Kluwer, Dordrecht, pp. 39–57.
Schnier, T. (1999) Evolved Representations and Their Use in Computational Creativity, Ph.D. Thesis, The Key Centre of Design Computing, University of Sydney.
Schön, D. A. (1983) The Reflective Practitioner: How Professionals Think in Action, Basic Books, New York.
Schön, D. A. and Wiggins, G. (1992) Kinds of seeing and their functions in designing, Design Studies, 13: 135–156.
Schulman, E. R. and Cox, C. V. (1997) How to write a Ph.D. dissertation, Annals of Improbable Research, 3(5): 8.
Scott, P. D., and Markovitch, S. (1989a) Learning Novel Domains Through Curiosity and Conjecture, Proceedings of International Joint Conference for Artificial Intelligence, Detroit, Michigan, pp. 669–674.
Scott, P. D., and Markovitch, S. (1989b) Uncertainty Based Selection of Learning Experiences, in Proceedings of The Sixth International Workshop on Machine Learning, Ithaca, New York, pp. 358–361.
Silberschatz, A. and Tuzhilin, A. (1996) What makes patterns interesting in knowledge discovery systems, IEEE Transactions on Knowledge and Data Engineering 8(6): 970–974.
Sims, K. (1991) Artificial evolution for computer graphics. Computer Graphics 25(4): 319-328.
Stanley, J. C. (1976) Computer simulation of a model of habituation. Nature, 261: 146–148.
Sternberg, R. J. (1988) The Nature of Creativity, Cambridge University Press, Cambridge, UK.
Sternberg, R. J. and Lubart, T. I. (1999) The Concept of Creativity: Prospects and Paradigms, in Robert J. Sternberg (ed.), The Handbook of Creativity, Cambridge University Press, Cambridge, UK, pp. 3–15.
Suwa, M., Gero, J. S. and Purcell, T. (1999) Unexpected discoveries and S-invention of design requirements: A key to creative designs, in J. S. Gero and M. L. Maher (eds), Computational Models of Creative Design IV, Key Centre of Design Computing and Cognition, University of Sydney, pp. 297–320.
Tabuada, P., Alves, P., and Gomes, J. (1998a) Artificial art through genetic algorithms, Proceedings of Genetic Algorithms and Artificial Life ’98 (GAAL’98), pp. 46–49.
Tabuada, P., Alves P., Gomes, J. and Rosa, A. (1998b) 3D artificial art by genetic algorithms, Proceedings of the Workshop on Evolutionary Design at Artificial Intelligence in Design ’98 (AID’98), pp. 18–21.
Taylor, C. W. (1988) Various approaches to the definition of creativity, in R. J. Sternberg (ed.), The Nature of Creativity, Cambridge University Press, Cambridge, UK, pp. 99–124.
Taylor, R. P., Micolich, A. P. and Jonas, D. (1999) Fractal analysis of Pollock’s drip paintings, Nature, 399:422.
Thaler, S. (1996) Neural networks that autonomously create and discover, PC AI, May/June.
Thompson, A. (2000) Explorations in design space: Unconventional electronics design through artificial evolution, IEEE Transactions on Evolutionary Computing, 3: 167–196.
Todd, S. and Latham, W. (1992) Evolutionary Art and Computers. Academic Press, London.
Tomlinson, P. and Gero, J. S. (1997), Emergent shape generation via the boundary
contour system, in R. Junge (ed.), CAAD Futures ’97, Kluwer, Dordrecht, pp.
865–874.
Turing, A. M. (1950) Computer machinery and intelligence, Mind, LIX(236): 433– 460.
Ullman, S. (1984) Visual routines, Cognition, 18: 97–159.
Waltz, D. (1998) The Importance of importance, AI Magazine, 20(3): 18–35
Wallas, G. (1926) The Art of Thought, Harcourt, Brace, Jovanovich, New York.
Weenink, D. (1997) Category ART: A variation on Adaptive Resonance Theory neural networks, Proceedings of the Institute of Phonetic Sciences 21, pp. 117– 129.
Whitfield, A. and Wiltshire, J. (1982) Design training and aesthetic evaluation: An intergroup comparison, Journal of Environmental Psychology, 2: 109–117.
Witbrock, M. and Reilly, S. N. (1999) Evolving genetic art, in Bentley, P. J. (ed.) Evolutionary Design by Computers, Morgan Kaufman, San Francisco, CA, pp. 251–259.
Wooldridge, M. J. and Jennings, N. R. (1995) Intelligent agents: Theory and practice, Knowledge Engineering Review, 10(2): 115–152.
Wooldridge, M. J. (1999) Intelligent agents, in G. Weiss (ed.), Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, MIT Press, Cambridge, MA, pp. 27–77.
Ziemke, T. (2000) Situated Neuro-Robotics and Interactive Cognition, Doctoral Dissertation, Department of Computer Science, University of Sheffield, UK
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