PROSPECTOR—A computer-based consultation system for mineral exploration

  • P. E. Hart
  • R. O. Duda
  • M. T. Einaudi


Pyrite Galena Inference Rule Dolomitization Carbonate Sediment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Botbel, J. M., Sinding-Larsen, R., McCammon, R. B., and Gott, G. B., 1976. Characteristic analysis of geochemical exploration data: U.S. Geological Survey Open File Report OF 77-349, U.S. Geological Survey, Reston, Virginia.Google Scholar
  2. Duda, R. O., Hart, P. E., and Nilsson, N. J., 1976. Subjective Bayesian methods for rule-based inference systems: National Computer Conference 1976 (AFIPS Conference Proceedings Vol. 45), p. 1075–1082.Google Scholar
  3. Duda, R. O., Hart, P. E., Nilsson, N. J., and Sutherland, G. L., 1977. Semantic network representations in rule-based inference systems: Technical Note 136. Artificial Intelligence Center. SRI International, Menlo Park, California, March 1977:in Pattern-Directed Inference Systems, D. A. Waterman and F. Hayes-Roth (eds.): Academic Press, in press.Google Scholar
  4. Ellis, J., Harris, D. P., and Van Wie, N., 1975. A subjective probability appraisal of uranium resources in the state of New Mexico: Grand Junction, Colorado, U.S. ERDA, December 97p.Google Scholar
  5. Harris, D. P., Berry, G., and Freyman, A., 1970. The methodlogy employed to estimate potential mineral supply of the Canadian northwest—an analysis based upon geologic opinion and systems simulation,in Proc. of the Ninth Int. Symposium on Decision Making: APCOM, June 14–19, 51 p.Google Scholar
  6. Harris, DeVerle P., in press. Mineral endowment, resources, and potential supply: Theory, methods for appraisal, and case studies.Google Scholar
  7. Hendrix, G. G., 1975. Expanding the utility of semantic networks through partitioning: Proc. Fourth Int. Joint Conf. on Artificial Intelligence, Tbilisi, Georgia, USSR, September 3–8.Google Scholar
  8. Linstone, H., and Turnoff, M., (eds.), 1975, The Delphi method—techniques and applications: Addison-Wesley Publishing Co., Reading, Massachusetts.Google Scholar
  9. Nilsson, N. J., 1971. Problem solving methods in artificial intelligence: McGraw-Hill Book Co., New York.Google Scholar
  10. Pauker, S. G., Gorry, G. A., Kassirer, J. P., and Schwartz, W. B., 1976. Towards the simulation of clinical cognition: Taking the present illness by computer: Amer. Jour. Medicine, v. 60, p. 981–996.CrossRefGoogle Scholar
  11. Pople, H., 1977, The formation of composite hypotheses in diagnostic problem solving: An exercise in synthetic Proc. 5th Int. Joint Conference on Artificial Intelligence: MIT, Cambridge, Massachusetts, p. 1030–1037.Google Scholar
  12. Raiffa, H., 1968. Decision analysis: Addison-Wesley Publishing Co., New York.Google Scholar
  13. Raphael, B., 1976. The thinking computer: W. H. Freeman and Co., San Francisco, California.Google Scholar
  14. Shortliffe, E. H., 1976. Computer-based medical consultations: MYCIN: American Elsevier Publishing Co., New York.Google Scholar
  15. Weiss, S. M., Kulikowski, C. A., and Safir, A., 1977, A model-based consultation system for the long-term management of glaucoma,in Proc. 5th Int. Joint Conference on Artiscial Intelligence: MIT. Cambridge, Massachusetts, p. 826–833.Google Scholar

Copyright information

© Plenum Publishing Corporation 1978

Authors and Affiliations

  • P. E. Hart
    • 1
  • R. O. Duda
    • 1
  • M. T. Einaudi
    • 2
  1. 1.Artificial Intelligence CenterSRI InternationalCaliforniaMemlo Park
  2. 2.Stanford UniversityStanford

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