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Creativity: a survey of AI approaches

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Abstract

In this paper we critically survey the AI programs that have been developed to exhibit some aspect of creative behaviour. We describe five necessary characteristics of models of creativity, and we apply these characteristics to help assess the programs surveyed. These characteristic features also provide a basis for a new theory of creative behavior: an emergent memory model. The survey is concluded with an assessment of an implementation of this latest theory.

Bill sings to Sarah. Sarah sings to Bill. Perhaps they will do other dangerous things together. They may eat lamb or stroke each other. They may chant of their difficulties and their happiness. They have love but they also have typewriters. That is interesting.

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References

  • I. D. Craig. Blackboard systems.Artificial Intelligence Review, 2: 79–118, 1988.

    Google Scholar 

  • R. Davis and D. Lenat.Knowledge Based Systems in Artificial Intelligence. McGraw-Hill, 1982.

  • Thomas G. Dietterich and Ryszard S. Michalski. Learning to predict sequences. In Ryszard S. Michalski, Jaime G. Carbonell, and Tom M. Mitchell, editors,Machine Learning: An Artificial Intelligence Approach, volume 2, chapter 4, pages 63–106. Morgan Kauffmann Publishers Inc., 1986.

  • K. Duncker. On problem solving.Psychological Monographs, 58(270), 1945.

  • J. L. Elman. Finding structure in time. Technical Report CRL Technical Report 8801, Center for Research in Language, University of California, San Diego, April 1988.

    Google Scholar 

  • S. L. Epstein. Learning and discovery: One system's search for mathematical knowledge.Computational Intelligence, 4(1): 42–53, 1988.

    Google Scholar 

  • M. Gardner. On playing the new eleusis, the game that simulates the search for truth.Scientific American, 237: 18–25, 1977.

    Google Scholar 

  • D. Gentner. Structure-mapping: A theoretical framework for analogy.Cognitive Science, 7: 155–170, 1983.

    Google Scholar 

  • Kenneth W. Haase Jr. Discovery systems. InProceedings of ECAI-86, volume 1, pages 546–555, 1986.

  • Douglas R. Hofstadter. The architecture of jumbo. In R. S. Michalski, editor,Proceedings of the International Machine Learning Workshop, pages 161–170, 1983.

  • Douglas R. Hofstadter. On the seeming paradox of mechanizing creativity. InMetamagical Themas, chapter 23, pages 526–546. Penguin, 1986a.

  • Douglas R. Hofstadter. Waking up from the Boolean dream. InMetamagical Themas, chapter 26, pages 631–665. Penguin, 1986b.

  • John H. Holland. Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In Ryszard S. Michalski, Jaime G. Carbonell, and Tom M. Mitchell, editors,Machine Learning: An Artificial Intelligence Approach, volume 2, chapter 20, pages 593–623. Morgan Kauffmann Publishers Inc., 1986.

  • P. N. Johnson-Laird. Reasoning, imagining and creating.Bulletin of the British Psychological Society, 40: 121–129, 1987.

    Google Scholar 

  • M. Keane. Analogical mechanisms.Artificial Intelligence Review, 2: 229–251, 1988.

    Google Scholar 

  • P. Langley, H. A. Simon, G. L. Bradshaw, and J. M. Zytkow.Scientific Discovery. MIT Press, Camb. Mass., 1987.

    Google Scholar 

  • Christopher Langton, editor.Artificial Life. Addison-Wesley, Redwood City, California, 1989.

    Google Scholar 

  • D. Lenat. The nature of heuristics.Artificial Intelligence, 19, 21, 1982.

    Google Scholar 

  • D. Lenat. Why AM and eurisko appear to work. InProceedings of the American Association of Artificial Intelligence, pages 236–240, 1983.

  • C. Martindale and D. Hines. Creativity and cortical activation during creative, intellectual and EEG feedback tasks.Biological Psychology, 3: 71–80, 1975.

    Google Scholar 

  • C. Martindale.Cognition and Consciousness. Dorsey Press, 1981.

  • James. R. Meehan. Tale-spin, an interactive program that writes stories. In5th International Joint Conference on Artificial Intelligence, pages 91–98, 1977.

  • Marsha Jean Ekstrom Meredith.Seek-Whence: a Model of Pattern Perception. PhD thesis, Indiana University, 1986.

  • Marvin Minsky.The Society of Mind. Picador, 1985.

  • Melanie Mitchell and Douglas R. Hofstadter. The emergence of understanding in a computer model of concepts and analogy-making.Physica D, 42: 322–334, 1990.

    Google Scholar 

  • Erik Thomas Mueller.Day-dreaming and Computation: A Computer Model of Everyday Creativity, Learning and Emotions in the Human Stream of Thought. PhD thesis, University of California, Los Angeles, 1987.

    Google Scholar 

  • Ajit Narayanan. What is it like to be a machine? In S. Torrance, editor,Mind and the Machine. Ellis Horwood, 1983.

  • R. M. Olton. Experimental studies of incubation: Searching for the elusive.Journal of Creative Behaviour, 13: 9–22, 1979.

    Google Scholar 

  • D. Pastre. MUSCADET: An automatic theorem proving system using knowledge and metaknowledge in mathematics.Artificial Intelligence, 38(3): 257–318, 1989.

    Google Scholar 

  • Jordan Pollack. Connectionism: Past, present, and future.Artificial Intelligence Review, 3(1): 3–22, 1989.

    Google Scholar 

  • Racter.The Policeman's Beard is Half-constructed. Warner Books, New York, NY, 1984. Racter is a program written by William Chamberlain.

  • J. D. Read and D. Bruce. Longitudinal tracking of difficult memory retrievals.Cognitive Psychology, 14: 280–300, 1982.

    Google Scholar 

  • G. D. Ritchie and F. K. Hanna. AM: A case study in AI methodology. In Partridge and Wilks, editors,The Foundations of AI: A sourcebook. Cambridge University Press, 1990.

  • Jonathan Rowe.Emergent Creativity: A Computational Study. PhD thesis, Computer Science Department, University of Exeter, UK, 1991.

    Google Scholar 

  • David E. Rumelhart. Notes on a schema for stories. In Collins, editor,Representation and Understanding: Studies in Cognitive Science. Academic Press, 1975.

  • Roger C. Schank and Robert P. Abelson.Scripts, Plans, Goals and Understanding. Erlbaum, Hillsdale NJ, 1977.

  • Roger C. Schank.Explanation Patterns. Lawrence Erlbaum Associates, 1986.

  • Mark J. Steedman. A generative grammar for jazz chord sequences.Music Perception, 2: 52–77, 1984.

    Google Scholar 

  • Robert Weisberg.Creativity: Genius and Other Myths. W. H. Freeman, 1986.

  • M. Yazdani. A computational model of creativity. In Richard Forsyth, editor,Machine learning: principles and techniques., chapter 9, pages 171–183. Chapman and Hall, 1989.

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Rowe, J., Partridge, D. Creativity: a survey of AI approaches. Artif Intell Rev 7, 43–70 (1993). https://doi.org/10.1007/BF00849197

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