Abstract
An important problem in computational scientific discovery is to identify, among the diversity of discovery programs written in various sciences, a commonality that will take a next step beyond the acknowledged general—but weak—framework of heuristic search. We characterize discovery in science as the generation of novel, interesting, plausible, and intelligible knowledge about the objects of study. We then analyze four current machine discovery programs in chemistry, medicine, mathematics, and linguistics according to how their design, or the circumstances of their application, heighten the chances of finding knowledge that has all four properties. Some general patterns emerge, although some strategies seem idiosyncratic. Our candidate for a commonality, which focuses on human factors, can be used pragmatically to evaluate and compare the designs of discovery programs that are intended to be used as collaborators by scientists.
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