Reference : Empirical Cognitive Study on Abstract Argumentation Semantics
Scientific congresses, symposiums and conference proceedings : Paper published in a journal
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/10993/37025
Empirical Cognitive Study on Abstract Argumentation Semantics
English
Cramer, Marcos[University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Guillaume, Mathieu[University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Education, Culture, Cognition and Society (ECCS) >]
7th International Conference on Computational Models of Argument
from 12-09-2018 to 14-09-2018
[en] In abstract argumentation theory, multiple argumentation semantics have been proposed that allow to select sets of jointly acceptable arguments from a given set of arguments based on the attack relation between arguments. The existence of multiple argumentation semantics raises the question which of these semantics predicts best how humans evaluate arguments, possibly depending on the thematic con- text of the arguments. In this study we report on an empirical cognitive study in which we tested how humans evaluate sets of arguments de- pending on the abstract structure of the attack relation between them. Two pilot studies were performed to validate the intended link between argumentation frameworks and sets of natural language arguments. The main experiment involved a group deliberation phase and made use of three different thematic contexts of the argument sets involved. The data strongly suggest that independently of the thematic contexts that we have considered, strong acceptance and strong rejection according to the CF2 and preferred semantics are a better predictor for human argument acceptance than the grounded semantics (which is identical to strong acceptance/rejection with respect to complete semantics). Furthermore, the data suggest that CF2 semantics predicts human argument acceptance better than preferred semantics, but the data for this comparison is limited to a single thematic context.
[University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
