An approach to characterize graded entailment of arguments through a label-based framework

Abstract

Argumentation theory is a powerful paradigm that formalizes a type of commonsense reasoning that aims to simulate the human ability to resolve a specific problem in an intelligent manner. A classical argumentation process takes into account only the properties related to the intrinsic logical soundness of an argument in order to determine its acceptability status. However, these properties are not always the only ones that matter to establish the argument's acceptability-there exist other qualities, such as strength, weight, social votes, trust degree, relevance level, and certainty degree, among others.In this work, we redefine the argumentative process to improve the analysis of arguments by considering their special features in order to obtain more refined results. Towards this end, we propose adding meta-level information to the arguments in the form of labels representing quantifiable data ranking over a range of fuzzy valuations. These labels are propagated through an argumentative graph according to the relations of support, conflict, and aggregation between arguments. Through this process we obtain final labels that are useful in determining argument acceptability.

A computational model of argumentation schemes for multi-agent systems

“…In the case in which the agent that receives the justify move from the opponent cannot itself reach the same conclusion, given the new information received (i.e., the agent does not have an acceptable argument for the subject of the dialogue anymore), the agent closes the dialogue, C iss4 . In the final case, the agent sends a new argument 14 to support the subject of the dialogue, C iss5 .…”

“…The idea of labelling arguments with meta-information used in this paper comes from Gabbay's work [24], further developed by other authors in [13,14,16,45,56]. While all that work focuses on modelling an argumentation framework in which the labels play an important role in the inference mechanism (mostly propagating strength of premises to the conclusions of arguments), here we propose a more modest use in which labels are used to make reference to argumentation schemes used to instantiate those arguments, pointing out the critical questions related to those instances of arguments.…”

Similarity notions in bipolar abstract argumentation

“…The authors highlight the use of t-conorms as a sensible way to obtain the uncertainty level of a conclusion supported by multiple arguments. Furthermore, in a general direction, Budán et al in [17,18,65] shows the application of these operations (c-norm and t-norm) within argumentation as a sensitive tool to diverse domains, such as decision support systems, recommendation systems, and legal systems.…”