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Machine discovery

  • Machine Discovery
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Abstract

Human and machine discovery are gradual problem-solving processes of searching large problem spaces for incompletely defined goal objects. Research on problem solving has usually focused on search of an “instance space” (empirical exploration) and a “hypothesis space” (generation of theories). In scientific discovery, search must often extend to other spaces as well: spaces of possible problems, of new or improved scientific instruments, of new problem representations, of new concepts, and others. This paper focuses especially on the processes for finding new problem representations and new concepts, which are relatively new domains for research on discovery.

Scientific discovery has usually been studied as an activity of individual investigators, but these individuals are positioned in a larger social structure of science, being linked by the “blackboard” of open publication (as well as by direct collaboration). Even while an investigator is working alone, the process is strongly influenced by knowledge and skills stored in memory as a result of previous social interaction. In this sense, all research on discovery, including the investigations on individual processes discussed in this paper, is social psychology, or even sociology.

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References

  • Anderson, J.R. (1993), Rules of the mind, Hilssdale, NJ: Erlbaum Associates.

    Google Scholar 

  • Bynum, W.F., Browne, E.J., Porter, R. (1981), Dictionary of The History of Science, Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Carbonell, J.G., Larkin, J.H., Reif, F (1983), Toward a general scientific reasoning engine, Technical Report, Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA.

    Google Scholar 

  • Child, J.M. (Ed.) (1916), The Geometrical lectures of Isaac Barrow, Chicago IL: Open Court Publishing Company.

    Google Scholar 

  • Ericsson, K.A., Staszewski, J. (1989), Skilled memory and expertise: Mechanisms of exceptional performance, in: D., Klahr and K., Kotovsky (Eds.), Complex Information Processing, pp 235–267. Hillsdale, NJ: Erlbaum Associates.

    Google Scholar 

  • Feigenbaum, E.A. and Simon, H.A. (1984), epam-like models of recognition and learning, Cognitive Science, 3, pp 305–336.

    Article  Google Scholar 

  • Fischer, P. and Zytkow, J.M. (1990), Discovering quarks and hidden structure, in: Proceedings of the International Symposium on Methodologies for Intelligent Systems '90.

  • Gobet, F. and Simon, H.A. (1994), The role of recognition processes and look-ahead search in grandmaster level chess, in: Complex Information Processing Technical Report, August 9, 1994, Department of Psychology, Carnegie Mellon University, Pittsburgh, PA.

    Google Scholar 

  • Kaplan, C. and Simon, H.A. (1990), In search of insight, Cognitive Psychology 22, pp 374–419.

    Article  Google Scholar 

  • Kocabas, S. (1991), Conflict resolution as discovery in particle physics, Machine Learning 6, pp 277–309.

    Google Scholar 

  • Hayes, J.R. and Simon, H.A. (1974), Understanding written problem instructions, in: L.W., Gregg (Ed.), Knowledge and Cognition, Potomac, MD: Earlbaum Associates.

    Google Scholar 

  • Holland, J.H., Holyoak, K.J., Nisbett, R.E. and Thagard, P.R. (1986), Induction: Processes of inference, learning, and discovery, Cambridge, MA: The MIT Press.

    Google Scholar 

  • Iwasaki, Y. and Simon, H.A. (1994), Causality and model abstraction, Artificial Intelligence 67, pp 143–194.

    Article  Google Scholar 

  • Korf, R. (1980), Toward a model of representation changes, Artificial Intelligence 14, pp 41–78.

    Article  Google Scholar 

  • Kulkarni, D. and Simon, H. A. (1988), The processes of scientific discovery: The strategy of experimentation, Cognitive Science 12, pp 139–176.

    Article  Google Scholar 

  • Langley, P., Simon, H. A., Bradshaw, G.L. and Zytkow, J.M. (1987), Scientific discovery: Computational explorations of the creative processes, Cambridge, MA: The MIT Press.

    Google Scholar 

  • Lenat, D. (1982), The nature of heuristics, Artificial Intelligence 19, pp 189–249.

    Article  Google Scholar 

  • Lenat, D. (1983), eurisko: A program that learns new heuristics and domain concepts, Artificial Intelligence 21, pp 61–98.

    Article  Google Scholar 

  • Low, C.M. and Iwasaki, Y. (1992), Device modeling environment: an interactive environment for modelling device behavior, Intelligent Systems Engineering 1, pp 115–145.

    Article  Google Scholar 

  • Neves, D.M. (1978), A computer program that learns algebraic procedures by examining examples and working problems in a textbook, in: Proceedings of the Second National Conference of the Canadian Society for Computational Studies of Intelligence, pp. 191–195.

  • Newell, A, Shaw, J.C. and Simon, H.A. (1956), Empirical explorations of the logic theory machine, in: Proceedings of the Western Joint Computer Conference, 218–230.

  • Novak, G.S. (1977), Representation of knowledge in a program for solving physics problems, in: Proceedings of the fifth International Joint Conference on Artificial Intelligence, pp 186–291.

  • Osherson, D.N., Stob, M. and Weinstein, S. et al. (1986), Systems that learn, Cambridge, MA: The MIT Press.

    Google Scholar 

  • Polya, G. (1945), How to solve it, Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Polya, G. (1954), Mathematics and plausible reasoning, Princeton, NJ. Princeton University Press, 2 vol.

    Google Scholar 

  • Popper, K.R. (1959), The logic of scientific discovery, London: Hutchison & Co.

    Google Scholar 

  • Qin, Y and Simon, H.A. (1990), Laboratory replication of scientific discovery processes, Cognitive Science 14, pp 281–312.

    Article  Google Scholar 

  • Richman, H.B., Staszewski, J.J. and Simon, H.A. (1995), Simulation of expert memory using epam IV, Psychological Review 102, pp.305–330.

    Article  Google Scholar 

  • Shen, W. (1990), Functional transformation in AI discovery systems, Artificial Intelligence 41, pp 257–272.

    Article  Google Scholar 

  • Shen, W. (1994), Autonomous learning from the environment, New York: W.H. Freeman & Co.

    Google Scholar 

  • Shen, W. and Simon, H.A. (1993), Fitness requirements for scientific theories containing recursive theoretical terms, British Journal for the Philosophy of Science 44, pp 641–652.

    Article  Google Scholar 

  • Simon, H.A. (1970), The axiomatization of physical theories, Philosophy of Science 37, pp 16–26.

    Article  Google Scholar 

  • Simon, H.A. and Groen, G.J. (1973), Ramsey eliminability and the testability of scientific theories, British Journal for the Philosophy of Science 24, pp 367–380.

    Article  Google Scholar 

  • Simon, H.A. (1983), Fitness requirements for scientific theories, British Journal for the Philosophy of Science 34, pp 355–365.

    Article  Google Scholar 

  • Simon, H. A. (1991), Comments on the symposium on “Computer discovery and the sociology of scientific knowledge”, Social Studies of Science 21, pp 143–148.

    Article  Google Scholar 

  • Spirtes, P., Glymour, C. and Scheines, R. (1993), Causation, prediction and search, New York NY: Springer-Verlag.

    Book  Google Scholar 

  • Tarski, A. (1983), Logic, semantics, meta-mathematics, Indianapolis, IN: Hackett Publishing Co.

    Google Scholar 

  • Valdés-Pérez, R.E. (1992), Theory-driven discovery of reaction pathways in the mechem system, in: Proceedings of the National Conference on Artificial Intelligence.

  • Valdés-Pérez, R.W. (forthcoming), Algebraic reasoning about reactions: Discovery of conserved properties in particle physics, To be published in: Journal of Computational Physics.

  • Zytkow, J.M. (1990), Deriving laws through analysis of processes and equations, in: P., Langley and J., Shrager (eds.), Computational Models of Discovery and Theory Formation, San Mateo, CA: Morgan Kaufmann Pub.

    Google Scholar 

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Simon, H. Machine discovery. Found Sci 1, 171–200 (1995). https://doi.org/10.1007/BF00124609

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