Abstract
AbstractAutomated scientific discovery is a discipline which lies at the boarder of artificial intelligence, natural sciences and philosophy of science, and deals with the application of artificial intelligence methods to scientific discovery. Its origins go back to the end of 1960s when a few AI researchers in the USA turned their attention to a domain often considered the realm of genius–scientific discovery. There have been at least three major research programs in the field, each of them having different objectives, concerns, and methodology: machine learning systems in the Turing tradition, normative theory of scientific discovery formulated by Herbert Simon's group, and the programs called HHNT, proposed by J. Holland, K. Holyoak, R. Nisbett, and P. Thagard. In this paper I briefly analyze those programs to figure out their methodological assumptions, goals, achievements and drawbacks.
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