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Mechanizing the Search for Explanatory Hypotheses

Published online by Cambridge University Press:  28 February 2022

Bruce G. Buchanan*
Affiliation:
Stanford University

Extract

Mechanized methods in science have attracted much attention among philosophers since Bacon. Of the many facets of scientific activity discovery has seemed most inscrutable. Until recently, Peirce and Hanson were the only ones to claim publicly that there can be rational methods for discovering hypotheses as well as for testing them. Their arguments, and more recent ones, are based on historical examples and analysis, and thus lack the convincingness of an existence proof. In this paper I take an empirical look at the question of whether there are rational methods of discovery and claim that computer programs provide a laboratory for experimentation on this question. Recent work in artificial intelligence, or AI, has produced programs capable of serious intellectual work in science. Results from AI will be used to show that there exist mechanized procedures for discovering hypotheses and that these methods often lead to plausible hypotheses (but do not guarantee always finding the correct hypothesis).

Type
Part III. Discovery, Heuristics, and Artificial Intelligence
Copyright
Copyright © 1983 Philosophy of Science Association

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Footnotes

1

I am grateful to Dr. Derek Sleeman, and Mr. Tom Dietterich for comments on early drafts of this paper, and to Dr. Lindley Darden for discussions. This work was supported in part by DARPA [Contract #MDA903-80-C-0107], ONR [Contract #N00014-79-C-0302], NLM [Contract #NLM 1 P01 LM03395], and NIH [Contract # NIH RR 00785-10.]

References

Barr, A. and Feigenbaum, E.A. (eds.). (1982). The Handbook of Artificial Intelligence. (Vols. I and II) . Los Altos, CA: Kaufmann.Google Scholar
Blum, R.L. (1981). Discovery and Representation of Causal Relationships from a Large Time-Oriented Clinical Database: The RX Project. Unpublished Ph.D. Dissertation, Stanford University. Xerox University Microfilms Publication Number DA8208819.Google Scholar
Brown, J.S. and Burton, R. (1978). “Diagnostic Models of Procedural Bugs in Basic Mathematical Skills.” Cognitive Science 2: 155-192.CrossRefGoogle Scholar
Buchanan, B.G. and Feigenbaum, E.A. (1978). “DENDRAL and Meta-DENDRAL: Their Applications Dimension.” Artificial Intelligence 11: 5-24.CrossRefGoogle Scholar
Buchanan, B.G. and Mitchell, T.M. (1978). “Model-Directed Learning of Production Rules.” In Pattern-Directed Inference Systems. Edited by Waterman, D.A. and Hayes-Roth, F.. New York: Academic Press. Pages 297-312.CrossRefGoogle Scholar
Buchanan, B.G. (1979). “Steps Toward Mechanizing Discovery.” Tech. Report HPP-79-28 Computer Science Dept., Stanford University. Presented at Pittsburgh University Conference on the Logic of Diagnosis.Google Scholar
Campbell, A.N.; Hollister, V.F.; Duda, R.O.; and Hart, P.E. (1982). “Recognition of a Hidden Mineral Deposit by an Artificial Intelligence Program.” Science 217: 927-929.CrossRefGoogle Scholar
Davis, R.; Shrobe, H. Hamscher, W.; Wieckert, K.; Shirley, M.; and Polit, S. (1982). “Diagnosis Based on Description of Structure and Function.” In Proceedings of the Second National Conference on Artificial Intelligence. Menlo Park, CA: American Association for Artificial Intelligence. Pages 137-142.Google Scholar
Engelmore, R.S. and Terry, A. (1979). “Structure and Function of the CRYSALIS System.” In Proceedings of the International Joint Conference on Artificial Intelligence. Pages 250-256.Google Scholar
Erman, L.D.; Hayes-Roth, F.; Reddy, D.R.; and Lesser, V.R. (1980). “The Hearsay-II Speech Understanding System: Integrating Knowledge to Resolve Uncertainty.” Computing Surveys 12: 213-253.CrossRefGoogle Scholar
Friedland, P. (1979). Knowledge-Based Experiment Design in Molecular Genetics. Unpublished. Ph.D. Dissertation, Stanford University. Xerox University Microfilms Publication Number DEM80-11638.Google Scholar
Genesereth, M. (1982). “Diagnosis Using Hierarchical Design Models.” In Proceedings of the Second National Conference on Artificial Intelligence. Menlo Park, CA: American Association for Artificial Intelligence. Pages 278-283.Google Scholar
Kunz, J.; Fallat, R.; McClung, D.; Osborn, J.; Votteri, B. Nii, H.; Aikins, J.; Fagan, L; and Feigenbaum, E. (1978). “A Physiological Rule-Based System for Interpreting Pulmonary Function Test Results.” Tech Report HPP-78-19 Computer Science Dept., Stanford University.Google Scholar
Langley, P.W (1979). “Rediscovering Physics with BACON. 3.” In Proceedings of the International Joint Conference on Artificial Intelligence. Pages 505-507.Google Scholar
Lindsay, R. Buchanan, B.G.; Feigenbaum, E.A.; and Lederberg, J. (1980). Applications of Artificial Intelligence for Organic Chemistry: The DENDRAL Protect. New York: McGraw Hill.Google Scholar
Miller, R.; Pople, H.E.; and Meyers, J.D. (1982). “Internist-I: An Experimental Computer-Based Diagnostic Consultant for General and Internal Medicine.” New England Journal of Medicine 307: 468-476.CrossRefGoogle Scholar
Newell, A. and Simon, H.A. (1976). “Computer Science as Empirical Inquiry: Symbols and Search. (The 1976 ACM Turing Lecture.)” Communications of the Association for Computing Machinery 19: 113-126.CrossRefGoogle Scholar
Nii, H.P.; Feigenbaum, E.A.; Anton, J.; and Rockmore, A. (1981). “Signal-to-Symbol Transformation: HASP/SIAP Case Study.” The Artificial Intelligence Magazine 3(2): 23-25.Google Scholar
Shortliffe, E.H. (1976). Computer Based Medical Consultations: MYCIN. New York: American Elsevier.Google Scholar
Sleeman, D. (1982). “Assessing Aspects of Competence in Basic Algebra.” In Intelligent Tutoring Systems. Edited by Sleeman, D., and Brown, J.. London: Academic Press. Pages 185-199.Google Scholar
Stefik, M. (1980). Planning with Constraints. Unpublished Ph.D. Dissertation, Stanford University. Xerox University Microfilms Publication Number DEM80-16868.Google Scholar
Weiss, S.; Kulikowski, C.; Amarel, S.; and Safir, A. (1979). “A Model-Based Method for Computer Aided Medical Decision Making.” Artificial Intelligence 11: 145-172.Google Scholar
Wipke, W.T.; Braun, H.; Smith, G.; Choplin, F.; and Sieber, W., (1977). “SECS—Simulation and Evaluation of Chemical Synthesis: Strategy and Planning.” In Computer Assisted Organic Synthesis. Edited by W.T. Wipke and W.J. House. Washington, D.C.: American Chemical Society. Pages 97-127.CrossRefGoogle Scholar