A Methodology for Incompleteness-Tolerant and Modular Gradual Semantics for Argumentative Statement Graphs
This work addresses the problem of handling incomplete information in structured argumentation for applications like explainable AI, representing an incremental advancement in the field.
The paper introduces a novel methodology for gradual semantics in argumentative statement graphs that accommodates incomplete information and modularly leverages existing gradual semantics for quantitative bipolar argumentation frameworks, demonstrating advantages over prior approaches through defined properties and instantiations.
Gradual semantics (GS) have demonstrated great potential in argumentation, in particular for deploying quantitative bipolar argumentation frameworks (QBAFs) in a number of real-world settings, from judgmental forecasting to explainable AI. In this paper, we provide a novel methodology for obtaining GS for statement graphs, a form of structured argumentation framework, where arguments and relations between them are built from logical statements. Our methodology differs from existing approaches in the literature in two main ways. First, it naturally accommodates incomplete information, so that arguments with partially specified premises can play a meaningful role in the evaluation. Second, it is modularly defined to leverage on any GS for QBAFs. We also define a set of novel properties for our GS and study their suitability alongside a set of existing properties (adapted to our setting) for two instantiations of our GS, demonstrating their advantages over existing approaches.