Abstract
In this paper we focus on the general problem of identifying and resolving conflicts such as inconsistency and incompleteness in scientific discovery. The underlying idea is that conflicts in empirical knowledge can lead to new discoveries, provided that they are clearly identified. Our observations are based on the behavior and the results of an incremental discovery program, BR-3. The system models the discoveries of a series of quantum laws by physicists in this century. BR-3's discoveries are directed by a small set of consistency and completeness constraints. We evaluate the system in terms of its knowledge representation, discovery operators, and their behavior, and we describe its relation to other work in scientific discovery.
Article PDF
Similar content being viewed by others
References
Davies, P. C. W. (1985). The forces of nature (2nd ed).Cambridge: Cambridge University Press.
Dean, T., & Boddy, M. (1987). Incremental causal reasoning. Proceedings of the Tenth International Conference on Artificial Intelligence (pp. 196–201). Milan, Italy: Morgan Kaufmann.
de Kleer, J. S. (1986). An assumption-based TMS. Artificial Intelligence, 28, 127–162.
Doyle, J. (1979). Choices without backtracking. Proceedings of the Fourth National Conference on Artificial In-telligence, (pp. 79–85). Austin, TX: Morgan Kaufmann.
Forbus, K. D. (1984). Qualitative process theory. Artificial Intelligence, 24, 85–168.
Godel, K. (1962). On formally undecidable propositions ofPrincipia Mathematica and related systems. Tr. by B. Meltzer. New York: Basic Books.
Karp, P. D. (1990). Hypothesis formation as design. In J. Shrager & P. Langley (Eds.), Computational models of scientific discovery and theory formation. San Mateo, CA: Morgan Kaufmann.
Kocabas, S. (1989). Functional categorization of knowledge: Applications in modeling scientific research and discovery. Doctoral Dissertation, Department of Electronic and Electrical Engineering, King's College London, University of London.
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., Simon, H. A., Bradshaw G. L., & Zytkow, J. M. (1987). Scientific discovery: Computational explora-tions of the creative processes. Cambridge, MA: The MIT Press.
Omnes, R. (1970). Introduction to particle physics. Tr. by G. Barton. London, Wiley Interscience.
O'Rorke, P., Morris, S., & Schulenburg, D. (1990). Theory formation by abstraction. In J. Shrager & P. Langley (Eds.), Computational models of scientific discovery and theory formation. San Mateo, CA: Morgan Kaufmann.
Rajamoney, S. A. (1990). A computational approach to theory revision. In J. Shrager & P. Langley (Eds.), Com-putational models of scientific discovery and theory formation. San Mateo, CA: Morgan Kaufmann.
Rose, D. (1989). Using domain knowledge to aid scientific theory revision. Proceedings of the Sixth International Workshop on Machine Learning (pp. 272–277). Ithaca, NY: Morgan Kaufmann.
Rose, D., & Langley, P. (1986). Chemical discovery as belief revision. Machine Learning, 1, 423–452.
Rose, D., & Langley, P. (1988). A hill-climbing approach to machine discovery. Proceedings of the Fifth Interna-tional Conference on Machine Learning (pp. 367–373). Ann Arbor, MI: Morgan Kaufmann.
Simon, H. A., & Lea, G. (1974). Problem solving and rule induction: A unified view. In L. Gregg (Ed.), Knowledge and cognition. Lawrence Erlbaum, Hillsdale, N. J.
Trefil, J. S. (1980). From atoms to quarks.London: The Athlone Press.
Zytkow, J. M., & Simon, H. A. (1986). A theory of historical discovery: The construction of componential models. Machine Learning, 1, 107–137.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Kocabas, S. Conflict Resolution as Discovery in Particle Physics. Machine Learning 6, 277–309 (1991). https://doi.org/10.1023/A:1022613811741
Issue Date:
DOI: https://doi.org/10.1023/A:1022613811741