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Australian Joint Conference on Artificial Intelligence

AI 2002: AI 2002: Advances in Artificial Intelligence pp 547–556Cite as

Convergency of Learning Process

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2557))

Abstract

This paper presents a learning process analysis on stability of learning in light of iterated belief revision. We view a learning process as a sequential belief change procedure. A learning policy is sought to guarantee every learning process leads to a complete knowledge about the world if the newly accepted information is the true fact on the world. The policy allows an agent to abandon the knowledge it has learned but requires a relatively moderate attitude to new information. It is shown that if new information is not always accepted in an extremely skeptical attitude and the changes of belief degrees follow the criterion of minimal change, any learning process for learning truth will converge to a complete knowledge state.

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Author information

Authors and Affiliations

  1. School of Computing and Information Technology, University ofWestern Sydney, Australia

    Dongmo Zhang

  2. School of Computer Science and Engineering, The University of New SouthWales, Australia

    Norman Foo

Authors
  1. Dongmo Zhang
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  2. Norman Foo
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Editor information

Editors and Affiliations

  1. Australian Defence Force Academy, University of New South Wales, ACT 2600, Canberra, Australia

    Bob McKay

  2. Computer Science Laboratory, Australian National University, RSISE Building, ACT 0200, Canberra, Australia

    John Slaney

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© 2002 Springer-Verlag Berlin Heidelberg

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Zhang, D., Foo, N. (2002). Convergency of Learning Process. In: McKay, B., Slaney, J. (eds) AI 2002: Advances in Artificial Intelligence. AI 2002. Lecture Notes in Computer Science(), vol 2557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36187-1_48

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  • DOI: https://doi.org/10.1007/3-540-36187-1_48

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00197-3

  • Online ISBN: 978-3-540-36187-9

  • eBook Packages: Springer Book Archive

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