Skip to main content

Creating theoretical terms for non-deterministic actions

  • Knowledge Representation II
  • Conference paper
  • First Online:
PRICAI'96: Topics in Artificial Intelligence (PRICAI 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1114))

Included in the following conference series:

Abstract

Theoretical terms play a central role in many scientific theories and includes such terms as quark and lepton in physics. However, such terms do not refer to observables or the properties of observables. Due to their central role in many scientific theories, formalisations and implementations of scientific discovery should account for theoretical terms. Few methods have been developed within the field of Artificial Intelligence to account for such terms and little work on correctness has been done. This paper will define a formal method for creating theoretical terms based on observationally non-deterministic actions. Further, this paper will define a class of possible worlds models for which the method is provably correct.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Taylor L. Booth. Sequential Machines and Automata Theory. John Wiley & Sons, 1967.

    Google Scholar 

  2. N. Foo. How theories fail — a preliminary report. In Proceedings of the National Conference on Information Technology, 1991.

    Google Scholar 

  3. N. Foo and Y. Zhang. Possible worlds semantics for events. In Proceedings of the 6th Australian Joint Conference on AI. World Scientific, 1993.

    Google Scholar 

  4. X. C. Ling. Inventing necessary theoretical terms to overcome representation bias. In Proceedings of Machine Learning 1992 Workshop on Inductive Learning. Morgan Kaufman, 1992.

    Google Scholar 

  5. Stephen Muggleton and Wray Buntine. Machine invention of first-order predicates by inverting resolution. In Proceedings of the 5th International Machine Learning Workshop, pages 339–352. Morgan Kaufman, 1988.

    Google Scholar 

  6. Stephen Muggleton. Inverting the resolution process. In J. E. Hayes and D. Michie, editors, Machine intelligence 12. 1989.

    Google Scholar 

  7. Maurice Pagnucco. Conjunctive versus disjunctive abduction — a pragmatic difference between abduction and inverse resolution. In Poster Proceedings of the Eighth Australian Joint Conference on Artificial Intelligence, pages 57–64, 1995.

    Google Scholar 

  8. W. Sellars. Science, Perception and Reality. Routledge and Kegan Paul, 1963.

    Google Scholar 

  9. W-M Shen. Discovery as automomous learning from the environment. In Machine Learning, 12, pages 145–165. Kluwer, 1993.

    Google Scholar 

  10. Wei-Min Shen and Herbert A. Simon. Rule creation and rule learning through environmental exploration. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence. Morgan Kaufman, 1989.

    Google Scholar 

  11. B. P. Zeigler. Theory of Modelling and Simulation. Wiley, 1976.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Norman Foo Randy Goebel

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kwok, R.B.H. (1996). Creating theoretical terms for non-deterministic actions. In: Foo, N., Goebel, R. (eds) PRICAI'96: Topics in Artificial Intelligence. PRICAI 1996. Lecture Notes in Computer Science, vol 1114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61532-6_43

Download citation

  • DOI: https://doi.org/10.1007/3-540-61532-6_43

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61532-3

  • Online ISBN: 978-3-540-68729-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics