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On the inference of sequences of functions

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Analogical and Inductive Inference (AII 1986)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 265))

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Abstract

We have shown that, in some sense, computers can be taught how to learn how to learn. The mathematical result constructed sequences of functions that were easy to learn, provided they were learned one at a time in a specific order. Furthermore, the sequences of functions constructed above are impossible to learn, by an algorithmic device, if the functions are not presented in the specified order.

As with any mathematical model, there is some question as to whether or not our result accurately captures the intuitive notion that it was intended to. Independently of how close our proof paradigm is to the intuitive notion of learning how to learn, if it were no were no formal analogue to the concept of machines that learn how to learn, then our result could not possibly be true. Our proof indicates not only that it is not impossible to program computers that learn based, in part, on their previous experiences, but that it is sometimes impossible to succeed without doing so.

Supported in part by NSA OCREAE Grant MDA904-85-H-0002

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Maryland, 20742, College Park, MD

    William I. Gasarch

  2. Department of Computer Science and Institute for Advanced Computer Studies, University of Maryland, 20742, College Park, MD

    Carl H. Smith

Authors
  1. William I. Gasarch
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  2. Carl H. Smith
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Editor information

Klaus P. Jantke

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© 1987 Springer-Verlag Berlin Heidelberg

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Gasarch, W.I., Smith, C.H. (1987). On the inference of sequences of functions. In: Jantke, K.P. (eds) Analogical and Inductive Inference. AII 1986. Lecture Notes in Computer Science, vol 265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-18081-8_83

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  • DOI: https://doi.org/10.1007/3-540-18081-8_83

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-18081-4

  • Online ISBN: 978-3-540-47739-6

  • eBook Packages: Springer Book Archive

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