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Southern African Conference for Artificial Intelligence Research

SACAIR 2022: Artificial Intelligence Research pp 187–201Cite as

Defeasible Justification Using the KLM Framework

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1734))

Abstract

The Kraus, Lehmann and Magidor (KLM) framework is an extension of Propositional Logic (PL) that can perform defeasible reasoning. The results of defeasible reasoning using the KLM framework are often challenging to understand. Therefore, one needs a framework within which it is possible to provide justifications for conclusions drawn from defeasible reasoning. This paper proposes a theoretical framework for defeasible justification in PL and a software tool that implements the framework. The theoretical framework is based on an existing theoretical framework for Description Logic (DL). The defeasible justification algorithm uses the statement ranking required by the KLM-style form of defeasible entailment known as Rational Closure. Classical justifications are computed based on materialised formulas (classical counterparts of defeasible formulas). The resulting classical justifications are converted to defeasible justifications, based on the input knowledge base. We provide an initial evaluation of the framework and the software tool by testing it with a representative example.

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Authors and Affiliations

  1. University of Cape Town, Cape Town, South Africa

    Steve Wang, Thomas Meyer & Deshendran Moodley

Authors
  1. Steve Wang
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  2. Thomas Meyer
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  3. Deshendran Moodley
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Corresponding author

Correspondence to Steve Wang .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Anban Pillay

  2. University of KwaZulu-Natal, Durban, South Africa

    Edgar Jembere

  3. University of Pretoria, Pretoria, South Africa

    Aurona Gerber

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Wang, S., Meyer, T., Moodley, D. (2022). Defeasible Justification Using the KLM Framework. In: Pillay, A., Jembere, E., Gerber, A. (eds) Artificial Intelligence Research. SACAIR 2022. Communications in Computer and Information Science, vol 1734. Springer, Cham. https://doi.org/10.1007/978-3-031-22321-1_13

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  • DOI: https://doi.org/10.1007/978-3-031-22321-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22320-4

  • Online ISBN: 978-3-031-22321-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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