Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-24T11:40:46.815Z Has data issue: false hasContentIssue false
  • English
  • Français

Computational models of scientific discovery

Published online by Cambridge University Press:  07 July 2009

Sakir Kocabas
Sakir Kocabas
Affiliation:
Marmara Scientific and Technological Research Centre, PK 21, Gebze, Kocaeli, Turkey
Get access Rights & Permissions [Opens in a new window]

Abstract

Computational modelling of scientific discovery is emerging as an important research field in artificial intelligence. Various computational systems modelling different aspects of scientific research and discovery have been developed. This paper looks at some of these models in order to examine how knowledge is organized in such systems, what forms of representation they have, how their methods of learning and representation are integrated, and the effects of representation on learning. The paper also describes the achievements and shortcomings of these systems, and discusses the obstacles in developing more comprehensive models.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 6 , Issue 4 , December 1991 , pp. 259 - 305
Copyright
Copyright © Cambridge University Press 1991

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Buchanan, BG and Feigenbaum, EA, 1978. “Dendral and Meta-Dentral: their application dimension” Artificial Intelligence 11 5–24.CrossRefGoogle Scholar
Buchanan, BG, Smith, DH, White, WC, Gritter, R, Feigenbaum, EA, Lederberg, J and Djerassi, C, 1976. “Applications of artificial intelligence for chemical inference. XXII. Automatic rule formation in mass spectrometry by means of the meta-DENDRAL program” Journal of the American Chemical Society 96 (6168).Google Scholar
Davies, PCW, 1985. The forces of Nature (2nd ed), Cambridge University Press, Cambridge.Google Scholar
de Kleer, JR, 1986. “An assumption-based TMS” Artificial Intelligence 8 127–162.CrossRefGoogle Scholar
Dietterich, TG and Michalski, RS, 1983. “A comparative view of selected methods for learning from examples”. In: Michalski, RS, Carbonell, JS and Mitchell, TM, eds., Machine Learning: An artificial intelligence approach Morgan Kaufmann, Los Altos, CA.Google Scholar
Forbus, KD, 1984. “Qualitative process theory” Artificial Intelligence 24 85–168.CrossRefGoogle Scholar
Friedland, P, 1979. “Knowledge-based experiment design in molecular genetics” Proceedings Sixth International Joint Conference on Artificial Intelligence285–287.Google Scholar
Holmes, FL, 1980. “Hans Krebs and the discovery of the Ornithine Cycle” Federation Proceedings 39 216–225.Google Scholar
Jones, R, 1986. “Generating predictions to aid scientific discovery process” Proceedings Fifth National Conference on Artificial Intelligence513–516.Google Scholar
Karp, PD, 1990. “Hypothesis formation as design”. In: Shrager, J and Langley, P, eds., Computational models of scientific discovery and theory formation Morgan Kaufmann, San Mateo, CA.Google Scholar
Kocabas, S, 1989. Functional categorization of knowledge: Applications in modeling scientific research and discovery PhD Thesis, Department of Electronic and Electrical Engineering, King's College London, University of London.Google Scholar
Kocabas, S, 1991a. “Conflict resolution as discovery in particle physics” Machine Learning 6 277–309.CrossRefGoogle Scholar
Kocabas, S, 1991b. “Homuncular learning and rule parallelism: An application to BACON” Proceedings International Conference on Control950–954, IEE Conference Publications,London.Google Scholar
Kuhn, TS, 1970. The Structure of Scientific Revolutions University of Chicago Press, Chicago, 16.Google Scholar
Kulkarni, D, 1989. The processes of scientific research: The strategy of experimentation Doctoral Dissertation. Department of Computer Science. Carnegie Mellon University, Pittsburgh, PA.Google Scholar
Kulkarni, D and Simon, HA, 1988. “The processes of scientific discovery” Cognitive Science 12 139–175.CrossRefGoogle Scholar
Kulkarni, D and Simon, HA, 1990. “The processes of scientific discovery” In: Shrager, J and Langley, P, eds., Computational models of scientific discovery and theory formation Morgan Kaufmann, San Mateo, CA.Google Scholar
Langley, P, 1978. “BACON1: A general discovery system” In: Proceedings Second National Conference of the Canadian Society for Computational Studies.Google Scholar
Langley, P, Simon, HA, Bradshaw, GL and Zytkow, JM, 1987. Scientific discovery: Computational explorations of the creative processes The MIT Press, Cambridge, MA.CrossRefGoogle Scholar
Lenat, DB, 1979. “On automated scientific theory formation: a case study using the AM program” In: Hayes, J, Michie, D and Mikulich, LI, eds., Machine Intelligence 9 251–283, Halstead, New York.Google Scholar
Lenat, DB, 1983a. “The role of heuristics in learning by discovery: three case studies” In: Michalski, RS, Carbonell, JG and Mitchell, TM, eds., Machine Learning: An artificial intelligence approach Morgan Kaufmann, Los Altos, CA.Google Scholar
Lenat, DB, 1983b. “EURISKO: a program that learns new heuristics and domain concepts” Artificial Intelligence 21 (1–2) 61–98.CrossRefGoogle Scholar
Lenat, DB, 1983c. “The role of heuristics in learning by discovery: three case studies” In: Michalski, RS, Carbonell, JC and Mitchell, TM, eds., Machine Learning: An artificial intelligence approach. Morgan Kaufmann, Los Altos, CA.Google Scholar
Lenat, DB, Prakash, M and Shepherd, M, 1986. “CYC: using common sense knowledge to overcome brittleness and knowledge acquisition bottlenecks” The AI Magazine 7 (4) 65–85.Google Scholar
Lenat, DB and Feigenbaum, EA, 1987. “On the thresholds of knowledge” Proceedings Tenth International Joint Conference on Artificial Intelligence. 1173–1182.Google Scholar
Lenat, DB and Guha, RV 1989. Building large knowledge based systems: Representation and inference in the CYC project Addison Wesley, Reading, MA.Google Scholar
Michalski, RS, 1983. “A theory and methodology of inductive learning” In Michalski, RS, Carbonell, JG and Mitchell, TM, eds., Machine learning: An artificial intelligence approach Morgan Kaufmann, Los Altos, CA.CrossRefGoogle Scholar
Michalski, RS, 1986. “Understanding the nature of learning: issues and research directions” In: Michalski, RS, Carbonell, JG and Mitchell, TM, eds., Machine Learning Morgan Kaufmann, Los Altos, CA.Google Scholar
Nilsson, NJ, 1965. Learning Machines: Foundations of trainable pattern-classifying systems McGraw-Hill, New York, NY.Google Scholar
Nordhausen, B and Langley, P, 1990. “An integrated approach to empirical discovery” In: Shrager, J and Langley, P, eds., Computational models of scientific discovery and theory formation Morgan Kaufmann, San Mateo, CA.Google Scholar
Nordhausen, B and Langley, P, 1987. “Towards an integrated discovery system” Proceedings Tenth International Joint Conference on Artificial Intelligence198–200.Google Scholar
Omnes, R, 1970. Introduction to particle physics (Translated by Barton, G) Wiley Interscience, London.Google Scholar
O'Rorke, P, Morris, S and Schulenburg, D, 1990. “Theory formation by abstraction” In: Shrager, J and Langley, P, eds., Computational models of scientific discovery and theory formation Morgan Kaufmann, San Mateo, CA.Google Scholar
Rajamoney, SA, 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.Google Scholar
Ritchie, GD and Hanna, FK, 1984. “AM: a case study in AI methodology” Artificial Intelligence 23 249–269.CrossRefGoogle Scholar
Rose, D and Langley, P, 1986. “Chemical discovery as belief revision” Machine Learning 1 423–452.CrossRefGoogle Scholar
Rose, D and Langley, P, 1988. “A hill-climbing approach to machine discovery” Proceedings Fifth International Conference on Machine Learning367–373. Morgan Kaufmann,Ann Arbor, MI.CrossRefGoogle Scholar
Shrager, J and Langley, P, 1990. “Computational approaches to scientific discovery” In: Shrager, J and Langley, P, eds., Computational models of scientific discovery and theory formation Morgan Kaufmann, San Mateo, CA.Google Scholar
Simon, HA and Lea, G, 1974. “Problem solving and rule induction: A unified view” In: Gregg, L, ed., Knowledge and cognition Erlbaum, Hillsdale, NJ.Google Scholar
Stefik, M, 1978. “Inferring DNA structures from segmentation data” Artificial Intelligence 11 85–114.CrossRefGoogle Scholar
Stefik, M, 1981a. “Planning with constraints (MOLGEN: Part 1)” Artificial Intelligence 2 111–139.CrossRefGoogle Scholar
Stefik, M, 1981b. “Planning and meta-planning (MOLGEN: Part 2)” Artificial Intelligence 2 141–169.CrossRefGoogle Scholar
Thagard, P, 1988. Computational philosophy of science The MIT Press, Cambridge, MA.CrossRefGoogle Scholar
Thagard, P, and Holyoak, K, 1985. “Discovering the wave theory of sound: Inductive inference in the context of problem solving” Proceedings Ninth International Joint Conference on Artificial Intelligence610–612.Google Scholar
Thagard, P and Nowak, G, 1990. “The conceptual structure of the geological revolution” In: Shrager, J and Langley, P, eds., Computational models of scientific discovery and theory formation Morgan Kaufmann, San Mateo, CA.Google Scholar
Tweney, RD, 1990. “Five questions for computationalists” In: Shrager, J and Langley, P, eds., Computational models of scientific discovery and theory formation Morgan Kaufmann, San Mateo, CA.Google Scholar
Zytkow, JM and Simon, HA, 1986. “A theory of historical discovery: the construction of componential models” Machine Learning 1 107–137.CrossRefGoogle Scholar
Zytkow, JM, 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 Kaufmann, San Mateo, CA.Google Scholar