Interpretability of Gradual Semantics in Abstract Argumentation

International audiencergumentation, in the field of Artificial Intelligence, is a for-malism allowing to reason with contradictory information as well as tomodel an exchange of arguments between one or several agents. For thispurpose, many semantics have been defined with, amongst them, grad-ual semantics aiming to assign an acceptability degree to each argument.Although the number of these semantics continues to increase, there iscurrently no method allowing to explain the results returned by thesesemantics. In this paper, we study the interpretability of these seman-tics by measuring, for each argument, the impact of the other argumentson its acceptability degree. We define a new property and show that thescore of an argument returned by a gradual semantics which satisfies thisproperty can also be computed by aggregating the impact of the otherarguments on it. This result allows to provide, for each argument in anargumentation framework, a ranking between arguments from the most to the least impacting ones w.r.t a given gradual semantic

Examining the Modelling Capabilities of Defeasible Argumentation and non-Monotonic Fuzzy Reasoning

Knowledge-representation and reasoning methods have been extensively researched within Artificial Intelligence. Among these, argumentation has emerged as an ideal paradigm for inference under uncertainty with conflicting knowledge. Its value has been predominantly demonstrated via analyses of the topological structure of graphs of arguments and its formal properties. However, limited research exists on the examination and comparison of its inferential capacity in real-world modelling tasks and against other knowledge-representation and non-monotonic reasoning methods. This study is focused on a novel comparison between defeasible argumentation and non-monotonic fuzzy reasoning when applied to the representation of the ill-defined construct of human mental workload and its assessment. Different argument-based and non-monotonic fuzzy reasoning models have been designed considering knowledge-bases of incremental complexity containing uncertain and conflicting information provided by a human reasoner. Findings showed how their inferences have a moderate convergent and face validity when compared respectively to those of an existing baseline instrument for mental workload assessment, and to a perception of mental workload self-reported by human participants. This confirmed how these models also reasonably represent the construct under consideration. Furthermore, argument-based models had on average a lower mean squared error against the self-reported perception of mental workload when compared to fuzzy-reasoning models and the baseline instrument. The contribution of this research is to provide scholars, interested in formalisms on knowledge-representation and non-monotonic reasoning, with a novel approach for empirically comparing their inferential capacity