The computer-aided discovery of scientific knowledge

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1532, Issue , 1998, Pages 25-39

  Langley, Pat ^a  

^a DaimlerChrysler Research and Technology North America Inc   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SCIENCE; COMPUTERS;

COMPUTER AIDED; DISCOVERY SYSTEMS; HUMAN INTERVENTION; HUMAN-COMPUTER COOPERATION; ORGANIZATIONAL FRAMEWORKS; SCIENTIFIC DISCOVERY; SCIENTIFIC KNOWLEDGE; SCIENTIFIC LITERATURE;

ARTIFICIAL INTELLIGENCE;

EID: 84949200339     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-49292-5_3     Document Type: Conference Paper

References (37)

1. 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.
2. 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.
3. 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.
4. 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.
5. Chung, F. (1988). The average distance is not more than the independence number. Journal of Graph Theory, 12, 229-235.
6. Fajtlowicz, S. (1988). On conjectures of Graffiti. Discrete Mathematics, 72, 113-118.
7. 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.
8. Finn, P., Muggleton, S., Page, D., & Srinivasan, A. (1998). Pharmacophore discovery using the inductive logic programming system Progol. Machine Learning, 30, 241-270.
9. Gillies, D. (1996). Artificial intelligence and scientific method. Oxford: Oxford Univerity Press.
10. 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.
11. Hunter, L. (1993). (Ed.). Artificial intelligence and molecular biology. Cambridge, MA: MIT Press.
12. 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.
13. 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.
14. 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.
15. Kocabas, S. (1991). Conflict resolution as discovery in particle physics. Machine Learning, 6, 277-309.
16. Kocabas, S., & Langley, P. (in press). Generating process explanations in nuclear astrophysics. Proceedings of the ECAI-98 Workshop on Machine Discovery. Brighton, England.
17. 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.
18. Langley, P. (1981). Data-driven discovery of physical laws. Cognitive Science, 5, 31-54.
19. Langley, P., & Simon, H. A. (1995). Applications of machine learning and rule induction. Communications of the ACM, 38, November, 55-64.
20. Langley, P., Simon, H. A., Bradshaw, G. L., & Żytkow, J. M. (1987). Scientific discovery: Computational explorations of the creative processes. Cambridge, MA: MIT Press.
21. 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.
22. 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.
23. 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.
24. 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.
25. 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.
26. 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.
27. Nordhausen, B., & Langley, P. (1993). An integrated framework for empirical discovery. Machine Learning, 12, 17-47.
28. 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.
29. Rose, D., & Langley, P. (1986). Chemical discovery as belief revision. Machine Learning, 1, 423-451.
30. Shrager, J., & Langley, P. (Eds.) (1990). Computational models of scientific discovery and theory formation. San Francisco: Morgan Kaufmann.
31. Todorovski, L., Džeroski, S., & Kompare, B. (in press). Modeling and prediction of phytoplankton growth with equation discovery. Ecological Modelling.
32. Valdés-Pérez, R. E. (1994). Human/computer interactive elucidation of reaction mechanisms: Application to catalyzed hydrogenolysis of ethane. Catalysis Letters, 28, 79-87.
33. Valdés-Pérez, R. E. (1995). Machine discovery in chemistry: New results. Artificial Intelligence, 74, 191-201.
34. 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.
35. 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
36. 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
37. Żytkow, J. M., & Simon, H. A. (1986). A theory of historical discovery: The construction of componential models. Machine Learning, 1, 107-137