Skip to main content
Log in

Hypothetical modeling: Methodology and application

  • Published:
Cybernetics and Systems Analysis Aims and scope

Conclusion

The article describes hypothetical modeling, a process that occupies an important place in theoretical and applied activity, but still remains outside the scope of mainstream research in computer and information sciences. The problems of hypothetical modeling constitute a well-defined, goal-directed class of problems, which can provide a fruitful field for the application of computer systems. The article has identified the main methods of hypothetical modeling and has introduced a number of new concepts and estimates that reflect the author's experience in this area. Our knowledge of hypothetical modeling requires further systematization and development of an efficient methodology. I would like to acknowledge the useful comments of Prof. Z. L. Rabinovich.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. S. Muggleton, "Inductive logic programming: derivations, successes and shortcomings, " SIGART Bull.,5, No. 1,5–11 (1994).

    Article  Google Scholar 

  2. E. Hunt, Artificial Intelligence [Russian translation], Mir, Moscow (1978).

  3. V. P. Gladun, Solution Planning [in Russian], Naukova Dumka, Kiev (1987).

  4. G. Platetsky-Shapiro and W. Frawley (eds.), Knowledge Discovery in Databases, AAAI Press, Menlo Park, CA (1991).

    Google Scholar 

  5. V. P. Gladun, Formation of New Knowledge [in Russian], Pedagog, Sofia (1994).

  6. V. Gladun and Z. Rabinovich, "Formation of the world model of artificial intelligence systems," in: Machine Intelligence, Vol. 9, 299–309, Ellis Horwood Ltd., Chichester, UK (1980).

    Google Scholar 

  7. V. Gladun and N. Vashchenko, "Local-static methods of knowledge formation," Kibernetika, No. 2, 62-74 (1995).

  8. D. P. Gorskii, Generalization and Cognition [in Russian], Mysl', Moscow (1985).

  9. D. Polya, Mathematical Discovery [Russian translation], Nauka, Moscow (1976).

  10. R. Hall, "Computational approaches to analogical reasoning: a comparative analysis," Artif. Intell.,3, No. 39, 39–120 (1989).

    Article  Google Scholar 

  11. S. Arikova and M. Haraguti, Analogy Theory: Knowledge Acquisition [Russian translation], Mir, Moscow (1990), pp. 262-297.

  12. V. Gladun, "Methods of thematic knowledge formation," Inform. Theories Appl., No. 3, 3–14 (1993).

  13. R. Schank, Conceptual Information Processing, Elsevier, New York (1980).

    Google Scholar 

  14. D. A. Pospelov, Modeling of Reasoning [in Russian], Radio i Svyaz', Moscow (1989).

  15. J. Quinlan, "Induction of decision trees," Machine Learning, No. 1, 81-106 (1986).

  16. R. Michalski, I. Mozetic, J. Hong, and N. Lavrac, "The multi-purpose incremental learning system AQ15 and its testing in application to three medical domains," Proc. 5th National Conf. Artif. Intell., Philadelphia, PA (1986), pp. 1041-1045.

  17. P. Clark and T. Niblett, "The CN2 induction algorithm," Machine Learning, No. 3, 261-283 (1989).

  18. M. Walker, "How feasible is automated discovery," IEEE Expert, 69-82 (Spring 1987).

  19. E. M. Savitskii, V. B. Gribulya, N. N. Kiseieva, and others, Computer Prediction in Material Science [in Russian], Nauka, Moscow (1990).

    Google Scholar 

  20. S. Muggleton, R. King, and M. Sternberg, "Protein secondary structure prediction using logic-based machine learning," Protein Engineering,5(7), 647–657 (1992).

    Article  Google Scholar 

Download references

Authors

Additional information

Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 10–20, January–February, 1997.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gladun, V.P. Hypothetical modeling: Methodology and application. Cybern Syst Anal 33, 7–15 (1997). https://doi.org/10.1007/BF02665935

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02665935

Keywords

Navigation