Scientific Discovery: Computational Explorations of the Creative Processes

By M. Stacey. 1988

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Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “…Harman, 1965; Hempel and Oppenheim, 1948; Hospers, 1946; Hume, 1748; James, 2003, 2003; Kaplan, 1964; Kim, Khanna and Koyejo, 2016; Kment, 2014; Langley, 1987; Lulia Nastasia and Rakow, 2010; Machamer, Darden, and Craver, 2000; Marr and Poggio, 1976; Maxwell, 1974; Maze, 1954; Meissner, 1960;…”
Mining association rules between sets of items in large databases (1993, methodology): “Examples of some well-known systems in this category include ABACUS[5], Bacon[7], and COPER[6].”
Statistical strategies for avoiding false discoveries in metabolomics and related experiments (2007, methodology): “…later discuss the importance of validation and cross-validation, we shall largely ignore the 'data-driven' literature on machine learning (e.g. (Langley et al., 1987; Weiss and Kulikowski, 1991; Anthony and Biggs, 1992; Hutchinson, 1994; Michie et al., 1994; Mitchell, 1997; Vapnik, 1998;…”
Creativity and Artificial Intelligence (1998, background): “Another example of AI creativity discovery is the BACON suite designed to model scientific discovery 15].”
Computational intelligence methods for rule-based data understanding (2004, unknown): “Methods of combining neural and symbolic knowledge, refining probabilistic rule bases, scientific law discovery [98], and data mining [30], are related closely to the applications of neural networks for extraction of logical rules.”
Computer Models of Creativity (2009, unknown): “Examples discussed below include drawing scientific generalizations that were first discovered centuries ago (Langley et al. 1987), or generating music of a type composed by long-dead musicians (Cope 2001, 2006) Even P-creativity in computers need not match all the previous achievements of human…”