Abstract
In this paper, we review AI research on computational discovery and its recent application to the discovery of new scientific knowledge. We characterize five historical stages of the scientific discovery process, which we use as an organizational framework in describing applications. We also identify five distinct steps during which developers or users can influence the behavior of a computational discovery system. Rather than criticizing such intervention, as done in the past, we recommend it as the preferred approach to using discovery software. As evidence for the advantages of such human-computer cooperation, we report seven examples of novel, computer-aided discoveries that have appeared in the scientific literature, along with the role that humans played in each case. We close by recommending that future systems provide more explicit support for human intervention in the discovery process
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Bruk, L. G., Gorodskii, S. N., Zeigarnik, A. V., Valdés-Pérez, R. E., & Temkin, O. N. (1998). Oxidative carbonylation of phenylacetylene catalyzed by Pd(II) and Cu(I): Experimental tests of forty-one computer-generated mechanistic hypotheses. Journal of Molecular Catalysis A: Chemical, 130, 29–40.
Cheeseman, P., Freeman, D., Kelly, J., Self, M., Stutz, J., & Taylor, W. (1988). Autoclass: A Bayesian classificiation system. Proceedings of the Fifth International Conference on Machine Learning (pp. 54–64). Ann Arbor, MI: Morgan Kaufmann.
Cheeseman, P., Goebel, J., Self, M., Stutz, M., Volk, K., Taylor, W., & Walker, H. (1989). Automatic classification of the spectra from the infrared astronomical satellite (IRAS) (Reference Publication 1217). Washington, DC: National Aeronautics and Space Administration.
Cheeseman, P., & Stutz, J. (1996). Bayesian classification (AutoClass): Theory and results. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, & R. Uthurusamy (Eds.), Advances in knowledge discovery and data mining. Cambridge, MA: MIT Press.
Chung, F. (1988). The average distance is not more than the independence number. Journal of Graph Theory, 12, 229–235.
Fajtlowicz, S. (1988). On conjectures of Graffiti. Discrete Mathematics, 72, 113–118.
Fayyad, U., Haussler, D., & Stolorz, P. (1996). KDD for science data analysis: Issues and examples. Proceedings of the Second International Conference of Knowledge Discovery and Data Mining (pp. 50–56). Portland, OR: AAAI Press.
Finn, P., Muggleton, S., Page, D., & Srinivasan, A. (1998). Pharmacophore discovery using the inductive logic programming system Progol. Machine Learning, 30, 241–270.
Gillies, D. (1996). Artificial intelligence and scientific method. Oxford: Oxford Univerity Press.
Goebel, J., Volk, K., Walker, H., Gerbault, F., Cheeseman, P., Self, M., Stutz, J., & Taylor, W. (1989). A Bayesian classification of the IRAS LRS Atlas. Astronomy and Astrophysics, 222, L5–L8.
Hunter, L. (1993). (Ed.). Artificial intelligence and molecular biology. Cambridge, MA: MIT Press.
Jones, R. (1986). Generating predictions to aid the scientific discovery process. Proceedings of the Fifth National Conference on Artificial Intelligence (pp. 513–517). Philadelphia: Morgan Kaufmann.
King, R. D., Muggleton, S. H., Srinivasan, A., & Sternberg, M. E. J. (1996). Structure-activity relationships derived by machine learning: The use of atoms and their bond connectives to predict mutagenicity by inductive logic programming. Proceedings of the National Academy of Sciences, 93, 438–442.
King, R. D., & Srinivasan, A. (1996). Prediction of rodent carcinogenicity bioassays from molecular structure using inductive logic programming. Environmental Health Perspectives, 104 (Supplement 5), 1031–1040.
Kocabas, S. (1991). Conflict resolution as discovery in particle physics. Machine Learning, 6, 277–309.
Kocabas, S., & Langley, P. (in press). Generating process explanations in nuclear astrophysics. Proceedings of the ECAI-98 Workshop on Machine Discovery. Brighton, England.
Kulkarni, D., & Simon, H. A. (1990). Experimentation in machine discovery. In J. Shrager & P. Langley (Eds.), Computational models of scientific discovery and theory formation. San Mateo, CA: Morgan Kaufmann.
Langley, P. (1981). Data-driven discovery of physical laws. Cognitive Science, 5, 31–54.
Langley, P., & Simon, H. A. (1995). Applications of machine learning and rule induction. Communications of the ACM, 38, November, 55–64.
Langley, P., Simon, H. A., Bradshaw, G. L., & Żytkow, J. M. (1987). Scientific discovery: Computational explorations of the creative processes. Cambridge, MA: MIT Press.
Lee, Y., Buchanan, B. G., & Aronis, J. M. (1998). Knowledge-based learning in exploratory science: Learning rules to predict rodent carcinogenicity. Machine Learning, 30, 217–240.
Lee, Y., Buchanan, B. G., Mattison, D. R., Klopman, G., & Rosenkranz, H. S. (1995). Learning rules to predict rodent carcinogenicity of non-genotoxic chemicals. Mutation Research, 328, 127–149.
Lee, Y., Buchanan, B. G., & Rosenkranz, H. S. (1996). Carcinogenicity predictions for a group of 30 chemicals undergoing rodent cancer bioassays based on rules derived from subchronic organ toxicities. Environmental Health Perspectives, 104(Supplement 5), 1059–1063.
Lenat, D.B. (1977). Automated theory formation in mathematics. Proceedings of the Fifth International Joint Conference on Artificial Intelligence (pp. 833–842). Cambridge, MA: Morgan Kaufmann.
Michalski, R. S., & Stepp, R. (1983). Learning from observation: Conceptual clustering. In R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.), Machine learning: An artificial intelligence approach. San Francisco: Morgan Kaufmann.
Mitchell, F., Sleeman, D., Duffy, J. A., Ingram, M. D., & Young, R. W. (1997). Optical basicity of metallurgical slags: A new computer-based system for data visualisation and analysis. Ironmaking and Steelmaking, 24, 306–320.
Nordhausen, B., & Langley, P. (1993). An integrated framework for empirical discovery. Machine Learning, 12, 17–47.
Pericliev, V., & Valdés-Pérez, R. E. (in press). Automatic componential analysis of kinship semantics with a proposed structural solution to the problem of multiple models. Anthropological Linguistics.
Rose, D., & Langley, P. (1986). Chemical discovery as belief revision. Machine Learning, 1, 423–451.
Shrager, J., & Langley, P. (Eds.) (1990). Computational models of scientific discovery and theory formation. San Francisco: Morgan Kaufmann.
Todorovski, L., Džeroski, S., & Kompare, B. (in press). Modeling and prediction of phytoplankton growth with equation discovery. Ecological Modelling.
Valdés-Pérez, R.E. (1994). Human/computer interactive elucidation of reaction mechanisms: Application to catalyzed hydrogenolysis of ethane. Catalysis Letters, 28, 79–87.
Valdés-Pérez, R.E. (1995). Machine discovery in chemistry: New results. Artificial Intelligence, 74, 191–201.
Valdés-Pérez, R.E. (1998). Why some programs do knowledge discovery well: Experiences from computational scientific discovery. Unpublished manuscript, School of Computer Science, Carnegie Mellon University, Pittsburgh,PA.
Zeigarnik, A. V., Valdés-Pérez, R. E., Temkin, O. N., Bruk, L. G., & Shalgunov, S. I. (1997). Computer-aided mechanism elucidation of acetylene hydrocarboxylation to acrylic acid based on a novel union of empirical and formal methods. Organometallics, 16, 3114–3127.
Żytkow, J.M. (1996). Incremental discovery of hidden structure: Applications in theory of elementary particles. Proceedings of the Thirteenth National Conference on Artificial Intelligence (pp. 750–756). Portland, OR: AAAI Press.
Żytkow, J. M., & Simon, H. A. (1986). A theory of historical discovery: The construction of componential models. Machine Learning, 1, 107–137.
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© 1998 Springer-Verlag Berlin Heidelberg
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Langley, P. (1998). The Computer-Aided Discovery of Scientific Knowledge. In: Arikawa, S., Motoda, H. (eds) Discovey Science. DS 1998. Lecture Notes in Computer Science(), vol 1532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49292-5_3
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DOI: https://doi.org/10.1007/3-540-49292-5_3
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