|
Buchanan BG and Feigenbaum EA, 1978. “Dendral and Meta-Dentral: their application dimension” Artificial Intelligence115–24.
Google 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 Society96 (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 Intelligence8127–162.
Google 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 approachMorgan Kaufmann, Los Altos, CA.
Google Scholar
|
|
Forbus KD, 1984. “Qualitative process theory” Artificial Intelligence2485–168.
Google 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 Proceedings39216–225.
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 formationMorgan 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 Learning6277–309.
Google 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 RevolutionsUniversity 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 Science12139–175.
Google 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 formationMorgan 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 processesThe MIT Press, Cambridge, MA.
Google 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 Intelligence9251–283, 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 approachMorgan Kaufmann, Los Altos, CA.
Google Scholar
|
|
Lenat DB, 1983b. “EURISKO: a program that learns new heuristics and domain concepts” Artificial Intelligence21 (1–2) 61–98.
Google 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 Magazine7 (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 RV1989. Building large knowledge based systems: Representation and inference in the CYC projectAddison 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 approachMorgan Kaufmann, Los Altos, CA.
Google Scholar
|
|
Michalski RS, 1986. “Understanding the nature of learning: issues and research directions” In: Michalski RS, Carbonell JG and Mitchell TM, eds., Machine LearningMorgan Kaufmann, Los Altos, CA.
Google Scholar
|
|
Nilsson NJ, 1965. Learning Machines: Foundations of trainable pattern-classifying systemsMcGraw-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 formationMorgan 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 formationMorgan 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 formationMorgan Kaufmann, San Mateo, CA.
Google Scholar
|
|
Ritchie GD and Hanna FK, 1984. “AM: a case study in AI methodology” Artificial Intelligence23249–269.
Google Scholar
|
|
Rose D and Langley P, 1986. “Chemical discovery as belief revision” Machine Learning1423–452.
Google 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.
Google 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 formationMorgan 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 Intelligence1185–114.
Google Scholar
|
|
Stefik M, 1981a. “Planning with constraints (MOLGEN: Part 1)” Artificial Intelligence2111–139.
Google Scholar
|
|
Stefik M, 1981b. “Planning and meta-planning (MOLGEN: Part 2)” Artificial Intelligence2141–169.
Google Scholar
|
|
Thagard P, 1988. Computational philosophy of scienceThe MIT Press, Cambridge, MA.
Google 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 formationMorgan 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 formationMorgan Kaufmann, San Mateo, CA.
Google Scholar
|
|
Zytkow JM and Simon HA, 1986. “A theory of historical discovery: the construction of componential models” Machine Learning1107–137.
Google 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 formationMorgan Kaufmann, San Mateo, CA.
Google Scholar
|
