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An inference engine for representing multiple theories

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Knowledge Representation and Organization in Machine Learning

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 347))

Abstract

The objective of this paper is to discuss some general requirements for knowledge representation formalisms used in learning systems. Some general examples of learning tasks are examined, and it is discussed how the extent of these tasks determines the requirements which must be fulfilled by the knowledge representation used in the learning program. It is argued that learning tasks in which the output of one learning stage is "looped back" as input to the next brings about specific requirements for the knowledge representation and for the maintenance of knowledge. A logic-based system is described which fulfills these requirements, allows the representation of the epistemological states of a learning program, and offers mechanisms necessary to solve "real world" learning tasks. As a case study, the possible use of a multiple theory representation is described in more detail.

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Authors and Affiliations

  1. Technische Universität Berlin Fachbereich Informatik, Projektgruppe KIT, Sekr. FR 5-12 Franklinstr. 28/29, 1000, Berlin 10, F.R.G.

    Werner Emde

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  1. Werner Emde
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Katharina Morik

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© 1989 Springer-Verlag Berlin Heidelberg

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Emde, W. (1989). An inference engine for representing multiple theories. In: Morik, K. (eds) Knowledge Representation and Organization in Machine Learning. Lecture Notes in Computer Science, vol 347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017221

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  • DOI: https://doi.org/10.1007/BFb0017221

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-50768-0

  • Online ISBN: 978-3-540-46081-7

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