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  1. Discovery without a ‘logic’ would be a miracle.Benjamin C. Jantzen - 2016 - Synthese 193 (10).
    Scientists routinely solve the problem of supplementing one’s store of variables with new theoretical posits that can explain the previously inexplicable. The banality of success at this task obscures a remarkable fact. Generating hypotheses that contain novel variables and accurately project over a limited amount of additional data is so difficult—the space of possibilities so vast—that succeeding through guesswork is overwhelmingly unlikely despite a very large number of attempts. And yet scientists do generate hypotheses of this sort in very few (...)
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  • Automated Discovery Systems, part 2: New developments, current issues, and philosophical lessons in machine learning and data science.Piotr Giza - 2021 - Philosophy Compass 17 (1):e12802.
    Philosophy Compass, Volume 17, Issue 1, January 2022.
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  • Automated discovery systems, part 1: Historical origins, main research programs, and methodological foundations.Piotr Giza - 2021 - Philosophy Compass 17 (1):e12800.
    Philosophy Compass, Volume 17, Issue 1, January 2022.
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  • Automated discovery systems and scientific realism.Piotr Giza - 2002 - Minds and Machines 12 (1):105-117.
    In the paper I explore the relations between a relatively new and quickly expanding branch of artificial intelligence –- the automated discovery systems –- and some new views advanced in the old debate over scientific realism. I focus my attention on one such system, GELL-MANN, designed in 1990 at Wichita State University. The program's task was to analyze elementary particle data available in 1964 and formulate an hypothesis (or hypotheses) about a `hidden', more simple structure of matter, or to put (...)
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  • Induction and the discovery of the causes of scurvy: a computational reconstruction.Vincent Corruble & Jean-Gabriel Ganascia - 1997 - Artificial Intelligence 91 (2):205-223.
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  • Is default logic a reinvention of inductive-statistical reasoning?Yao-Hua Tan - 1997 - Synthese 110 (3):357-379.
    Currently there is hardly any connection between philosophy of science and Artificial Intelligence research. We argue that both fields can benefit from each other. As an example of this mutual benefit we discuss the relation between Inductive-Statistical Reasoning and Default Logic. One of the main topics in AI research is the study of common-sense reasoning with incomplete information. Default logic is especially developed to formalise this type of reasoning. We show that there is a striking resemblance between inductive-statistical reasoning and (...)
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