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Proceedings of the Computational Methods in Systems and Software

CoMeSySo 2021: Software Engineering Application in Informatics pp 499–510Cite as

Inference Methods for Evaluable Knowledge Bases

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 232)

Abstract

This paper investigates inference from evaluable knowledge bases without reasoning by contradiction. These knowledge bases are comprised of non-Horn rules with partial predicates and functions, some of them are defined as recursive functions. This inference corresponds to model elimination without the reduction rule, which is equivalent to forward and backward chaining extended with rule contrapositives, to input resolution, and to constrained resolution without factoring including its ordered variant.

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  • DOI: 10.1007/978-3-030-90318-3_41
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Authors and Affiliations

  1. Synstretch, Framingham, MA, USA

    Alexander Sakharov

Authors
  1. Alexander Sakharov
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Correspondence to Alexander Sakharov .

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Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Dr. Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Prof. Petr Silhavy

  3. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Prof. Zdenka Prokopova

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Sakharov, A. (2021). Inference Methods for Evaluable Knowledge Bases. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Software Engineering Application in Informatics. CoMeSySo 2021. Lecture Notes in Networks and Systems, vol 232. Springer, Cham. https://doi.org/10.1007/978-3-030-90318-3_41

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