An empirical evaluation of the inferential capacity of defeasible argumentation, non-monotonic fuzzy reasoning and expert systems
作者：
Highlights：
• Replicable comparison of inferences produced by non-monotonic reasoning approaches.
• Assessment of the ill-defined construct of mental workload using real-world data.
• Defeasible argumentation presented a superior inferential capacity of mental workload.
• Use of defeasible argumentation in practical fields seldom reported in the literature.
• Robust results analysed in two real-world contexts with three knowledge bases.
摘要
•Replicable comparison of inferences produced by non-monotonic reasoning approaches.•Assessment of the ill-defined construct of mental workload using real-world data.•Defeasible argumentation presented a superior inferential capacity of mental workload.•Use of defeasible argumentation in practical fields seldom reported in the literature.•Robust results analysed in two real-world contexts with three knowledge bases.
论文关键词：Defeasible argumentation,Argumentation theory,Explainable artificial intelligence,Non-monotonic reasoning,Fuzzy logic,Expert systems,Mental workload
论文评审过程：Received 1 August 2019, Revised 10 December 2019, Accepted 17 January 2020, Available online 18 January 2020, Version of Record 31 January 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113220
