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Change of Representation and Inductive Bias pp 267–308Cite as

Declarative Bias: An Overview

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Part of the book series: The Kluwer International Series in Engineering and Computer Science ((SECS,volume 87))

Abstract

This paper describes and places in context a continuing research program aimed at constructing effective, autonomous learning systems. We emphasize the role of knowledge that the system itself possesses in generating and selecting among inductive hypotheses. Inductive learning has often been characterized as a search in a hypothesis space for hypotheses consistent with observations. It is shown that committing to a given hypothesis space is equivalent to believing a certain logical sentence — the declarative bias. We show how many kinds of declarative bias can be relatively efficiently represented and derived from background knowledge, and discuss possibilities and problems for building complete learning systems.

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Author information

Authors and Affiliations

  1. Computer Science Division, University of California, Berkeley, CA, 94720, USA

    Stuart J. Russell

  2. IBM T. J. Watson Research Center, P.O. Box 704, Yorktown Heights, NY, 10598, USA

    Benjamin N. Grosof

Authors
  1. Stuart J. Russell
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  2. Benjamin N. Grosof
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Editors and Affiliations

  1. Philips Laboratories, 345 Scarborough Rd., Briarcliff Manor, NY, 10510, USA

    D. Paul Benjamin

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© 1990 Kluwer Academic Publishers

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Russell, S.J., Grosof, B.N. (1990). Declarative Bias: An Overview. In: Benjamin, D.P. (eds) Change of Representation and Inductive Bias. The Kluwer International Series in Engineering and Computer Science, vol 87. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1523-0_16

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  • DOI: https://doi.org/10.1007/978-1-4613-1523-0_16

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4612-8817-6

  • Online ISBN: 978-1-4613-1523-0

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