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Abstraction Within Partial Deduction for Linear Logic

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Artificial Intelligence and Symbolic Computation (AISC 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3249))

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Abstract

Abstraction has been used extensively in Artificial Intelligence (AI) planning, human problem solving and theorem proving. In this article we show how to apply abstraction within Partial Deduction (PD) formalism for Linear Logic (LL). The proposal is accompanied with formal results identifying limitations and advantages of the approach.

We adapt a technique from AI planning for constructing abstraction hierarchies, which are then exploited during PD. Although the complexity of PD for propositional LL is generally decidable, by applying abstraction the complexity is reduced to polynomial in certain cases.

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Author information

Authors and Affiliations

  1. Department of Computer and Information Science, Norwegian University of Science and Technology,  

    Peep Küngas

Authors
  1. Peep Küngas
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Editor information

Editors and Affiliations

  1. Research Institute for Symbolic Computation, Johannes Kepler University, Linz, Austria

    Bruno Buchberger

  2. Department of Computer Science, University College London, Gower Street, WC1E 6BT, London, UK

    John Campbell

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© 2004 Springer-Verlag Berlin Heidelberg

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Küngas, P. (2004). Abstraction Within Partial Deduction for Linear Logic. In: Buchberger, B., Campbell, J. (eds) Artificial Intelligence and Symbolic Computation. AISC 2004. Lecture Notes in Computer Science(), vol 3249. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30210-0_6

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  • DOI: https://doi.org/10.1007/978-3-540-30210-0_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23212-4

  • Online ISBN: 978-3-540-30210-0

  • eBook Packages: Springer Book Archive

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