An Approach to Characterize Graded Entailment of Arguments through a Label-based Framework
This work addresses the problem of enhancing argumentation theory for AI systems by allowing more nuanced reasoning, though it appears incremental as it builds on existing paradigms.
The paper tackles the limitation of classical argumentation processes that only consider logical soundness by proposing a label-based framework to incorporate additional qualities like strength and certainty, enabling graded entailment of arguments.
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.