Inferential Models of Mental Workload with Defeasible Argumentation and Non-monotonic Fuzzy Reasoning: a Comparative Study
Year: 2019
Type: conference-paper
Abstract: Inferences through knowledge driven approaches have been researched extensively in the field of Artificial Intelligence. Among such approaches argumentation theory has recently shown appealing properties for inference under uncertainty and conflicting evidence. Nonetheless, there is a lack of studies which examine its inferential capacity over other quantitative theories of reasoning under uncertainty with real-world knowledge-bases. This study is focused on a comparison between argumentation theory and non-monotonic fuzzy reasoning when applied to modeling the construct of human mental workload (MWL). Different argument-based and non-monotonic fuzzy reasoning models, aimed at inferring the MWL imposed by a selection of learning tasks, in a third-level context, have been designed. These models are built upon knowledge-bases that contain uncertain and conflicting evidence provided by human experts. An analysis of the convergent and face validity of such models has been performed. Results suggest a superior inferential capacity of argument-based models over fuzzy reasoning-based models.
Authors: Lucas Rizzo, Luca Longo
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1677423749, G5789963951
OpenAlex ID: W2911682283