TY - JOUR
T1 - Exploring the potential of defeasible argumentation for quantitative inferences in real-world contexts
T2 - 28th Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2020
AU - Rizzo, Lucas
AU - Dondio, Pierpaolo
AU - Longo, Luca
N1 - Publisher Copyright:
© 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
PY - 2020
Y1 - 2020
N2 - Argumentation has recently shown appealing properties for inference under uncertainty and conflicting knowledge. However, there is a lack of studies focused on the examination of its capacity of exploiting real-world knowledge bases for performing quantitative, case-by-case inferences. This study performs an analysis of the inferential capacity of a set of argument-based models, designed by a human reasoner, for the problem of trust assessment. Precisely, these models are exploited using data from Wikipedia, and are aimed at inferring the trustworthiness of its editors. A comparison against non-deductive approaches revealed that these models were superior according to values inferred to recognised trustworthy editors. This research contributes to the field of argumentation by employing a replicable modular design which is suitable for modelling reasoning under uncertainty applied to distinct real-world domains.
AB - Argumentation has recently shown appealing properties for inference under uncertainty and conflicting knowledge. However, there is a lack of studies focused on the examination of its capacity of exploiting real-world knowledge bases for performing quantitative, case-by-case inferences. This study performs an analysis of the inferential capacity of a set of argument-based models, designed by a human reasoner, for the problem of trust assessment. Precisely, these models are exploited using data from Wikipedia, and are aimed at inferring the trustworthiness of its editors. A comparison against non-deductive approaches revealed that these models were superior according to values inferred to recognised trustworthy editors. This research contributes to the field of argumentation by employing a replicable modular design which is suitable for modelling reasoning under uncertainty applied to distinct real-world domains.
KW - Argumentation Theory
KW - Computational Trust
KW - Defeasible Argumentation
KW - Explainable Artificial Intelligence
KW - Non-monotonic Reasoning
UR - http://www.scopus.com/inward/record.url?scp=85099391602&partnerID=8YFLogxK
U2 - 10.21427/jb0g-bs68
DO - 10.21427/jb0g-bs68
M3 - Conference article
AN - SCOPUS:85099391602
SN - 1613-0073
VL - 2771
SP - 37
EP - 48
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
Y2 - 7 December 2020 through 8 December 2020
ER -