AI and Philosophy of Science

Abstract

Yapay Zeka ve Bilim Felsefesi

Abstract

Bilimsel buluşlar üzerine yapay zekada yapılan yeni araştırmalar bilim konusunda daha önce gözardı edilmiş olan bir dizi önemli hususu ortaya çıkarmıştır. Yapay zekacı bilim adamları tarafından bilim tarihindeki buluşların farklı yönlerini araştırmak üzere geliştirilen bir dizi bilgisayar modeli, hipotez oluşturma, hipotez testi ve değerlendirmesi bilimsel araştırma faaliyetinin sadece küçük bir parçasıdır. Bu çalışma yapay zeka açısından, bilimsel yaratıcılık, bilimsel araştırmanın süreçleri, bilimsel araştırmanın boyutları ve bilginin araştırmadaki rolünü incelemektedir

References

1. Boden, M. (1990), The creative mind, Sphere Books, London.
2. Darden. L. (1987), “Viewing the history of science as compiled hindsight”, The AI Magazine, 8. No. 2. 33-42.
3. ——, (1991). Theory change in science: Strategies from Mendelian genetics, Oxford University Press, N.Y.
4. Engelmore, R. and Morgan T. (1988), Blackboard systems, Addi- son Wesley.
5. Forbus, K. D. (1984), “Qualitative process theory”, Artificial Intel- ligence, 24, 85-168.
6. Hayes-Roth, B. (1993), Architectural foundations for real-time performance in intelligent systems, in David, J-M., Krivine, J-P., and Simmons, R. eds. Second Generation Expert Systems, Springer- Verlag, New York.
7. Karmiloff-Smith, A. (1990), “Constraints of representational change: Evidence from children’s drawing”, Cognition, 34.
8. Kocabaş, S. (1991), “Conflict resolution as discovery in particle physics”, Machine Learning, Vol 6, No 3, 277-309.

9. ——, (1992a), “Functional categorization of knowledge”, AAAI Spring Symposion Series, 25-27 March 1992, Stanford, CA.
10. ——, (1992b), “Elements of scientific research: Modeling disco- veries in oxie superconductivity”, Proceedings of the ML92 Work- shop on Machine Discovery, 63-70.
11. ——, (1992c), “Evaluation of discovery systems”, Proceedings of the ML92 Workshop on Machine Discovery, 168-171.
12. ——, and Langley, P. (1995), “Integration of research tasks for modeling discoveries in particle physics”, in Working Notes of 1995 Spring Symposium Series, AAAI Press, CA.
13. Kuhn, T. S. (1970), The structure of scientific revolutions, The Uni- versity of Chicago Press, Chicago.
14. Kulkarni, D. and Simon, H. (1988). The processes of scientific dis- covery, Cognitive Science, 12, 139-175.
15. Langley, P., Simon, H., Bradshaw, G., and Zykow, J., (1987). Sci- entific discovery: Exploration of the creative processes, MIT Press.
16. Lenat, D. B. (1979), “On automated scientific theory formation: A case study using the AM program”, in Hayes, J., Michie., D., and Mikulich, D. I. eds. Machine Intelligence, 9, 251-283, Halstead, New York.
17. ——, (1983), “EURISKO: A program that learns new heuristics and domain concepts”, Artificial Intelligence 21, 61-98.
18. ——, and Feigenbaum, E. (1987), “On the thresholds of know- ledge”, Proceedings of the Tenth International Joint Conference on Artificial Intelligence, 1173-1182.
19. Maslow, A. H. (1966), The psychology of science: A reconnessaince, Harper and Row Publishers. N.Y.
20. Nordhausen, B. and Langley, P. (1987), “Towards an integrated discovery system”, Proceedings of the Tenth Internatiohal Joint Conference on Artificial Intelligence, 198-200.
21. O’Rorke, P., Morris, S. and Schulenburg, D. (1990), “Theory for- mation by abstration”, in Shrager, J., and Langley P. eds. Computa- tional models of scientific discovery and theory formation, Morgan Kaufmann, San Mateo, CA.
22. Rajamoney, S. A. (1990), “A computational approach to theory revision”, in Shrager, J., and Langley P., eds. Computational models of scientific discovery and theory formation, Morgan Kaufmann, San Mateo, CA.
23. Rose, D. and Langley, P. (1986), “Chemical discovery as belief re- vision”, Machine Learning, 1, 423-452.
24. Shrager, J., and Langley, P. Eds. (1990), “Computational appro- aches to scientific discovery”, in Shrager, J., and Langley P., eds. Computational models of scientific discovery and theory formation. Morgan Kaufmann, San Mateo, CA.
25. Simon, H. A. (1992), “Scientific discovery as problem solving: Reply to critics”. International Studies in the Philosophy of Science 6 (1): 69-88.
26. Thagard, P. (1988), Computational philosophy of science, The MIT Press, Cambridge, MA.
27. Thagard, P. and Holyoak, K. (1985), “Discovering the wave theory of sound: inductive inference in the context of problem solving”, Proceedings of the Ninth International Joint Conference on Artifici- al Intelligence, 610-612.
28. Thagard, P. and Nowak, G. (1990). “The conceptual structure of the geological revolution”, in Shrager, J., and Langley P., eds. Com- putational models of scientific discovery and theory formation Mor- gan Kaufmann, San Mateo, CA.
29. Valdes-Perez, R. E. (1994), “Algebraic reasoning about reactions Discovery of conserved properties in particle physics”, Machine Learning 17 (1), 47-68.
30. ——, (1995), “Machine discovery in chemistry: New results”. Ar- tificial Intelligence 74 (1), 191-201.
31. Zytkow, J. M. (1987). “Combining many searches in the FAH- RENHEIT discovery system”. Proc. 4th International Workshop on Machine Learning, Morgan Kaufmann, CA. 281-287.
32. ——, (1990), “Deriving laws through analysis of processes and equations”, in Shrager, J., and Langley P., eds. Computational models of scientific discovery and theory formation. Morgan Kauf- mann, San Mateo, CA.
33. ——, and Simon, H. (1986), “A theory of historical discovery: The construction of componential models”. Machine Learning, 1, 107- 137.