Hostname: page-component-8448b6f56d-c47g7 Total loading time: 0 Render date: 2024-04-16T11:47:23.861Z Has data issue: false hasContentIssue false
  • English
  • Français

Computer science research on scientific discovery

Published online by Cambridge University Press:  07 July 2009

Raúl E. Valdés-Pérez
Raúl E. Valdés-Pérez
Affiliation:
Computer Science Department and Center for Light Microscope Imaging and Biotechnology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

This article is an essay on directions and methodology in computer-science oriented research on scientific discovery. The essay starts by reviewing briefly some of the history of computing in scientific reasoning, and some of the results and impact that have been achieved. The remainder analyses some of the goals of this field, its relations with sister fields, and the practical applications of this analysis to evaluating research quality, reviewing, and methodology. An earlier review in this journal (Kocabas 1991b) analysed scientific discovery programs in terms of their designs, achievements and shortcomings; the focus here is research directions, evaluation and methodology, all from the viewpoint of computer science.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 11 , Issue 1 , March 1996 , pp. 57 - 66
Copyright
Copyright © Cambridge University Press 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bobrow, DG and Hayes, PJ, 1985, “Artificial intelligence – where are we?” Artificial Intelligence 25 (3) 375–415.CrossRefGoogle Scholar
Buchanan, B, Smith, D, White, W, Gritter, R, Feigenbaum, E, Lederberg, J and Djerassi, C, 1976, “Applications of artificial intelligence for chemical inference. 22. automatic rule formation in mass spectrometry by means of the meta–dendral program” Journal of the American Chemical Society 98 (20) 6168–6178.CrossRefGoogle Scholar
Carbonell, JG, 1992, “Machine learning: A maturing field” Machine Learning 9 5–7.CrossRefGoogle Scholar
Crevier, D, 1993, Al: The Tumultuous History of the Search for Artificial Intelligence, Basic Books.Google Scholar
Dunbar, K, 1995, “How scientists really reason: Scientific reasoning in real-world laboratories.” In: The Nature of Insight, 365–395, MIT Press.Google Scholar
Gelernter, H, 1963, “Realization of a geometry-theorem proving machine.” In: Feigenbaum, EA and Feldman, J, editors, Computers and Thought, McGraw-Hill.Google Scholar
Gordon, A, Edwards, P, Sleeman, D and Kodratoff, Y, 1994, “Scientific discovery in a space of structural models: An example from the history of solution chemistry.” In: Proceedings of the 16th Conference of the Cognitive Science Society 381–386.CrossRefGoogle Scholar
Hearn, AC, 1990, Future Directions for Research in Symbolic Computation, Siam, Philadelphia.Google Scholar
Hendrickson, J and Toczko, A, 1989, “SYNGEN program for synthesis design: basic computing techniques” Journal of Chemical Information and Computer Sciences 29 (3) 137–145.CrossRefGoogle Scholar
Holmes, FL, 1980, “Hans Krebs and the discovery of the ornithine cycle.” In: Proc of 63rd Annual Meeting of the Federation of American Societies for Experimental Biology 39, Symposium on Aspects of the History of Biochemistry.Google Scholar
Klahr, D and Dunbar, K, 1988, “Dual space search during scientific reasoning” Cognitive Science 12 1–48.CrossRefGoogle Scholar
Kocabas, S, 1991a, “Conflict re/ution as discovery in particle physics” Machine Learning 6 (3), 05, 277–309.CrossRefGoogle Scholar
Kocabas, S, 1991b, “Computational models of scientific discovery”, Knowledge Engineering Review 6 (4) 259–305.CrossRefGoogle Scholar
Kulkarni, D and Simon, H, 1988, “The processes of scientific discovery: The strategy of experimentation” Cognitive Science 12 139–175.CrossRefGoogle Scholar
Langley, P, 1986, “Editorial: On machine learning” Machine Learning 1 5–10.CrossRefGoogle Scholar
Langley, P and Michalski, RS, 1986, “Editorial: Machine learning and discovery” Machine Learning 1 363–366.CrossRefGoogle Scholar
Langley, P, Simon, H, Bradshaw, G and Zytkow, J. 1987, Scientific Discovery. Computational Explorations of the Creative Processes, MIT Press.CrossRefGoogle Scholar
Lea, G and Simon, HA, 1974, “Problem /ving and rule induction: A uniuleld view.” In: Gregg, LW, editor, Knowledge and Cognition, Lawrence Erlbaum.Google Scholar
Lindsay, R, Buchanan, B, Feigenbaum, E and Lederberg, J, 1980, Applications of Artificial Intelligence for Organic Chemistry: The Dendral Project, McGraw-Hill.Google Scholar
Lindsay, R, Buchanan, B, Feigenbaum, E amd Lederberg, J, 1993, “DENDRAL: a case study of the first expert system for scientific hypothesis formation” Artificial Intelligence 61(2) 06, 209–261.CrossRefGoogle Scholar
Mackenzie, D, 1995, “The automation of proof: a historical and sociological exploration” IEEE Annals of the History of Computing 17 (3) 7–29.CrossRefGoogle Scholar
Newell, A, 1983, “The heuristic of George Polya and its relation to artificial intelligence.” In: Groner, R, Groner, M and Bischof, W, editors, Methods of Heuristics, Lawrence Erlbaum.Google Scholar
Newell, A and Simon, HA, 1972, Human Problem /ving, Prentice-Hall.Google Scholar
Newell, A and Simon, HA, 1976, “Computer science as empirical inquiry: Symbols and search” Communications of the ACM 19 111–126.CrossRefGoogle Scholar
Oliver, JE, 1991, The Incomplete Guide to the Art of Discovery, Columbia University Press.CrossRefGoogle Scholar
Salmon, W, 1966, The Foundations of Scientific Inference, University of Pittsburgh Press. In: Shrager, J and Langley, P, editors, Computational Models of Scientific Discovery and Theory formation, Morgan Kaufmann.Google Scholar
Simon, HA, 1987, “Why should machines learn” Machine Learning 1 25–38.Google Scholar
Spirtes, P, Glymour, C and Schemes, R, 1993, Causation, Prediction, and Search, Springer-Verlag.CrossRefGoogle Scholar
Stevenson, D, 1994, “Science, computational science, and computer science: At a crossroads” Communications of the ACM 37 (12) 12, 85–96.CrossRefGoogle Scholar
Thagard, P and Nowak, G, 1990, “The conceptual structure of the geological revolution.” In: Shrager, J and Langley, P, editors, Computational Models of Scientific Discovery and Theory Formation 27–172, Morgan Kaufman.Google Scholar
Tweney, RD, 1990, “Five questions for computationalists.” In: Shrager, J and Langley, P, editors, Computational Models of Scientific Discovery and Theory Formation 471–484, Morgan Kaufmann.Google Scholar
Valdés-Perez, RE, 1994a, “Algebraic reasoning about reactions: Discovery of conserved properties in particle physics” Machine Learning 17 (1) 47–68.CrossRefGoogle Scholar
Valdes-Perez, RE, 1994b, “Human/computer interactive elucidation of reaction mechanisms: Application to catalyzed hydrogenolysis of ethane” Catalysis Letters 28(1) 09, 79–87.CrossRefGoogle Scholar
Valdés-Perez, RE, 1995a, “Generic tasks of scientific discovery.” In: Working Notes of the Spring Symposium on Systematic Methods of Scientific Discovery, AAAI Technical Reports.Google Scholar
Valdés-Perez, RE, 1995b, “Machine discovery in chemistry: New results” Artificial Intelligence 74(1) 03, 191–201.CrossRefGoogle Scholar
Valdés-Perez, RE, 1995c, “Machine discovery praxis” Foundations of Science 1(2) 219–224.Google Scholar
Valdés-Perez, RE, 1995d, “Some recent human/computer discoveries in science and what accounts for them” Al Magazine 16 (3) Fall, 37–44.Google Scholar
Valdés-Perez, RE and Erdmann, M, 1994, “Systematic induction and parsimony of phenomenological conservation laws” Computer Physics Communications 83(–3) 171–180.CrossRefGoogle Scholar
Zytkow, J, 1993, Machine Learning 12 (1). Special double issue on machine discovery.Google Scholar