Skip to main content
SpringerLink
Log in
Book cover

Portuguese Conference on Artificial Intelligence

EPIA 1993: Progress in Artificial Intelligence pp 324–339Cite as

Evaluating evidence for motivated discovery

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 727))

Abstract

Inductive discovery can be said to proceed through the accumulation of evidence in attempts to refute the current theory. Typically, this has involved a straight choice between the falsification of a theory and its continued use, with little or no consideration given to the possibility of error. This paper addresses the problems of error and evidence evaluation. It considers a variety of different kinds of error and uncertainty that arise through interaction with an external world, in both scientific discovery and other inductive reasoning. In response, it proposes a generally applicable model for the evaluation of evidence under differing motivations, and describes its implementation in the MID system for motivated discovery.

This work was carried out under a SERC studentship award.

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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. F. Crick. What Mad Pursuit: A Personal View of Scientific Discovery. Basic Books, New York, NY, 1988.

    Google Scholar 

  2. R. Garigliano, A. Bokma, and D. Long. A model for learning by source control. In B. Bouchon, L. Saitta, and R. R. Yager, editors, Lecture Notes in Computer Science, 313. Springer-Verlag, 1988.

    Google Scholar 

  3. G. Hon. Towards a typology of errors: An epistemological view. Studies in History and Philosophy of Science, 20(4):469–504, 1989.

    Google Scholar 

  4. P. D. Karp. Hypothesis formation as design. In J. Shrager and P. Langley, editors, Computational Models of Scientific Discovery and Theory Formation, pages 275–317. Morgan Kaufmann, San Mateo, CA, 1990.

    Google Scholar 

  5. Z. Kunda. The case for motivated reasoning. Psychological Bulletin, 108(3):480–498, 1990.

    Google Scholar 

  6. P. Langley, H. A. Simon, G. L. Bradshaw, and J. M. Zytkow. Scientific Discovery: Computational Explorations of the Creative Processes. MIT Press, Cambridge, Mass., 1987.

    Google Scholar 

  7. D. Leake and A. Ram. Goal-drivern learning: Fundamental issues and symposium report. Technical Report 85, Cognitive Science Program, Indiana University, Bloomington, Indiana, 1993.

    Google Scholar 

  8. R. Lelouche and S. Doublait. Qualitative reasoning with bluff and beliefs in a multi-actor environment. International Journal of Man-Machine Studies, 36:149–165, 1992.

    Google Scholar 

  9. M. M. Luck. Motivations in inductive discovery: Reasoning for knowledge and action. Research Note RN/92/80, Department of Computer Science, University College London, 1992.

    Google Scholar 

  10. M. M. Luck. Motivated Inductive Discovery. PhD thesis, University College London, In preparation.

    Google Scholar 

  11. P. Maes. A bottom-up mechanism for behaviour selection in an artificial creature. In J. A. Meyer and S.W. Wilson, editors, Proceedings of the First International Conference on Simulation of Adaptive Behaviour: From Animals to Animats, pages 238–246. MIT Press/Bradford Books, 1991.

    Google Scholar 

  12. S. Marsh. Trust and reliance in multi-agent systems: A preliminary report. In Pre-Proceedings of the Fourth European Workshop on Modelling Autonomous Agents in a Multi-Agent World, 1992.

    Google Scholar 

  13. S. A. Rajamoney. Explanation-based Theory Revision: An Approach to the Problems of Incomplete and Incorrect Theories. PhD thesis, Department of Computer Science, University of Illinois, Urbana, Illinois, 1989.

    Google Scholar 

  14. A. Sloman. Motives, mechanisms, and emotions. Cognition and Emotion, 1(3):217–233, 1987.

    Google Scholar 

  15. J. Zytkow, J. Zhu, and R. Zembowicz. The first phase of real-world discovery: Determining repeatability and error of experiments. In Proceedings of the Ninth International Conference on Machine Learning, pages 480–485, Aberdeen, 1992.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University College London, Gower St, WC1E 6BT, London, UK

    Michael M. Luck

Authors
  1. Michael M. Luck
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Miguel Filgueiras Luís Damas

Rights and permissions

Reprints and permissions

Copyright information

© 1993 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Luck, M.M. (1993). Evaluating evidence for motivated discovery. In: Filgueiras, M., Damas, L. (eds) Progress in Artificial Intelligence. EPIA 1993. Lecture Notes in Computer Science, vol 727. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57287-2_58

Download citation

  • DOI: https://doi.org/10.1007/3-540-57287-2_58

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-57287-9

  • Online ISBN: 978-3-540-48036-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation