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Robot Baby 2001

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Algorithmic Learning Theory (ALT 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2225))

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Abstract

In this paper we claim that meaningful representations can be learned by programs, although today they are almost always designed by skilled engineers. We discuss several kinds of meaning that representations might have, and focus on a functional notion of meaning as appropriate for programs to learn. Specifically, a representation is meaningful if it incorporates an indicator of external conditions and if the indicator relation informs action. We survey methods for inducing kinds of representations we call structural abstractions. Prototypes of sensory time series are one kind of structural abstraction, and though they are not denoting or compositional, they do support planning. Deictic representations of objects and prototype representations of words enable a program to learn the denotational meanings of words. Finally, we discuss two algorithms designed to find the macroscopic structure of episodes in a domain-independent way.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Massachusetts, Amherst, USA

    Paul R. Cohen

  2. Department of Computer Science, University of Maryland, Baltimore County, USA

    Tim Oates

  3. Department of Mathematics, Imperial College, London, UK

    Niall Adams

  4. Department of Psychology, University of Massachusetts, Amherst, USA

    Carole R. Beal

Authors
  1. Paul R. Cohen
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  2. Tim Oates
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  3. Niall Adams
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  4. Carole R. Beal
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Editor information

Editors and Affiliations

  1. IBM, Thomas J.Watson Research Center, Room 33-237, P.O.Box 218, 10598, Yorktown, NY, USA

    Naoki Abe

  2. Dept.of Electrical Engineering and Computer Science, Tufts University, 161 College Ave., 02155, Medford, MA, USA

    Roni Khardon

  3. Medizinische Universität zu Lübeck,Inst.für Theoretische Informatik, Wallstr.40, 23560, Lübeck, Germany

    Thomas Zeugmann

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© 2001 Springer-Verlag Berlin Heidelberg

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Cohen, P.R., Oates, T., Adams, N., Beal, C.R. (2001). Robot Baby 2001. In: Abe, N., Khardon, R., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2001. Lecture Notes in Computer Science(), vol 2225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45583-3_4

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  • DOI: https://doi.org/10.1007/3-540-45583-3_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42875-6

  • Online ISBN: 978-3-540-45583-7

  • eBook Packages: Springer Book Archive

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