David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Perspectives on Science 16 (2):pp. 121-136 (2008)
Ever since 1956 when details of the Logic Theorist were published by Newell and Simon, a large literature has accumulated on computational models and theories of the creative process, especially in science, invention and design. But what exactly do these computational models/theories tell us about the way that humans have actually conducted acts of creation in the past? What light has computation shed on our understanding of the creative process? Addressing these questions, we put forth three propositions: (I) Computational models of the creative process are fundamentally flawed as theories of human creativity. Rather, the universal power of computational models lies elsewhere: (II) Computational models of particular acts of creation can serve as effective experiments to test universal hypotheses about creative processes and mechanisms; and (III) Computation-based architectures of the creative mind provide metaphorical frameworks that, like all good metaphors, can serve as rich sources of insight into aspects of the creative process. In this paper, we provide evidence for these three propositions by drawing upon some particular episodes in the cognitive history of science, technology, and art.
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References found in this work BETA
Subrata Dasgupta (2003). Multidisciplinary Creativity: The Case of Herbert A. Simon. Cognitive Science 27 (5):683-707.
Deepak Kulkarni & Herbert A. Simon (1988). The Processes of Scientific Discovery: The Strategy of Experimentation. Cognitive Science 12 (2):139-175.
Allen Newell (1982). The Knowledge Level. Artificial Intelligence 18:81-132.
Yulin Qin & Herbert A. Simon (1990). Laboratory Replication of Scientific Discovery Processes. Cognitive Science 14 (2):281-312.
Citations of this work BETA
Dongming Xu (2010). Beyond Simon's Means-Ends Analysis: Natural Creativity and the Unanswered 'Why' in the Design of Intelligent Systems for Problem-Solving. [REVIEW] Minds and Machines 20 (3):327-347.
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