Scientific Discovery: Computational Explorations of the Creative Processes

by Pat Langley, Herbert A. Simon

Scientific discovery is often regarded as romantic and creative -- and hence unanalyzable -- whereas the everyday process of verifying discoveries is sober and more suited to analysis. Yet this fascinating exploration of how scientific work proceeds argues that however sudden the moment of discovery may seem, the discovery process can be described and modeled. Using the methods and concepts of contemporary information-processing psychology (or cognitive science) the authors develop a series of artificial-intelligence programs that can simulate the human thought processes used to discover scientific laws. The programs -- BACON, DALTON, GLAUBER, and STAHL -- are all largely data-driven, that is, when presented with series of chemical or physical measurements they search for uniformities and linking elements, generating and checking hypotheses and creating new concepts as they go along. Scientific Discovery examines the nature of scientific research and reviews the arguments for and against a normative theory of discovery; describes the evolution of the BACON programs, which discover quantitative empirical laws and invent new concepts; presents programs that discover laws in qualitative and quantitative data; and ties the results together, suggesting how a combined and extended program might find research problems, invent new instruments, and invent appropriate problem representations. Numerous prominent historical examples of discoveries from physics and chemistry are used as tests for the programs and anchor the discussion concretely in the history of science.

344 pages, Paperback

First published January 1, 1987

Reviews

[Lucille Nguyen · Rating 4 out of 5]

A fascinating attempt to utilize production systems/expert systems to generate inductive discoveries in the sciences, particularly historical ones like Newton's laws and Ohms Law. Additionally, it shows the power of heuristic methods to cluster concepts. However, as many reviewers noted at the time (among them Malcolm Forester, Joseph Sneed, and Wolfgang Balzer) this capacity is rather ungeneralizable to the cases of messy data. Nonetheless, the extension of the formal symbol system hypothesis to scientific discovery is itself rather fascinating.

[Gary Robert Gress · Rating 2 out of 5]

An early (1987) AI attempt at typically too high a level. In terms of true contribution and progress, it's probably not much better than curve fitting.