A qualitative investigation of the degree of explainability of defeasible argumentation and non-monotonic fuzzy reasoning

Lucas Rizzo, Luca Longo

Research output: Contribution to journalConference articlepeer-review

Abstract

Defeasible argumentation has advanced as a solid theoretical research discipline for inference under uncertainty. Scholars have predominantly focused on the construction of argument-based models for demonstrating non-monotonic reasoning adopting the notions of arguments and conflicts. However, they have marginally attempted to examine the degree of explainability that this approach can offer to explain inferences to humans in real-world applications. Model explanations are extremely important in areas such as medical diagnosis because they can increase human trustworthiness towards automatic inferences. In this research, the inferential processes of defeasible argumentation and non-monotonic fuzzy reasoning are meticulously described, exploited and qualitatively compared. A number of properties have been selected for such a comparison including understandability, simulatability, algorithmic transparency, post-hoc interpretability, computational complexity and extensibility. Findings show how defeasible argumentation can lead to the construction of inferential non-monotonic models with a higher degree of explainability compared to those built with fuzzy reasoning.

Original languageEnglish
Pages (from-to)138-149
Number of pages12
JournalCEUR Workshop Proceedings
Volume2259
DOIs
Publication statusPublished - 2018
Event26th AIAI Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2018 - Dublin, Ireland
Duration: 6 Dec 20187 Dec 2018

Keywords

  • Argumentation theory
  • Defeasible argumentation
  • Explainable artificial intelligence
  • Fuzzy reasoning
  • Non-monotonic reasoning

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