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Learning plan abstractions

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GWAI-92: Advances in Artificial Intelligence

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 671))

Abstract

Abstraction has been identified as a powerful means to reduce the complexity of planning problems. In this paper, a formal model and a method are described for learning abstract plans from concrete plans. In this model, the problem of plan abstraction is decomposed into finding a state abstraction mapping and a sequence abstraction mapping. The definition of an abstract planning world and a generic state abstraction theory allows a totally different terminology to be introduced for the description of the abstract plans. Thereby, not only generalizations but real abstractions can be established which require a shift in representation. With the described explanation-based learning procedure PABS, deductively justified abstractions are constructed which are tailored to the application domain described by the theory. The proposed abstraction methodology is applied to learn from machine-oriented programs. An abstract program can be acquired on a higher programming level which is characterized through the use of abstract data types instead of machine-oriented data representations.

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Author information

Authors and Affiliations

  1. German Research Center for Artificial Intelligence, Erwin-Schrödinger-Str., Bau 57, D-6750, Kaiserslautern, Germany

    Ralph Bergmann

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  1. Ralph Bergmann
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Hans Jürgen Ohlbach

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© 1993 Springer-Verlag Berlin Heidelberg

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Bergmann, R. (1993). Learning plan abstractions. In: Jürgen Ohlbach, H. (eds) GWAI-92: Advances in Artificial Intelligence. Lecture Notes in Computer Science, vol 671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0019004

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  • DOI: https://doi.org/10.1007/BFb0019004

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-56667-0

  • Online ISBN: 978-3-540-47626-9

  • eBook Packages: Springer Book Archive

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