AILOJun 16, 2020

On SCC-recursiveness in Quantitative Argumentation

arXiv:2006.08880v2
Originality Incremental advance
AI Analysis

This work addresses a theoretical and practical problem for researchers in argumentation frameworks by extending a known property to a quantitative domain, though it is incremental as it adapts existing concepts.

The paper tackled the gap in applying SCC-recursiveness to quantitative argumentation by demonstrating its suitability for fuzzy extension semantics in fuzzy argumentation frameworks, resulting in a sound and complete algorithm that reduces computational efforts for large SCCs.

Abstract argumentation is a reasoning model for evaluating arguments based on various semantics. SCC-recursiveness is a sophisticated property of semantics that provides a general schema for characterizing semantics through the decomposition along strongly connected components (SCCs). While this property has been extensively explored in various qualitative frameworks, it has been relatively neglected in quantitative argumentation. To fill this gap, we demonstrate that this property is well-suited to fuzzy extension semantics, which is a quantitative generalization of classical semantics in fuzzy argumentation frameworks (FAF). We tailor the SCC-recursive schema to enable the characterization of fuzzy extension semantics through the recursive decomposition of an FAF along its SCCs. Our contributions are twofold. Theoretically, we show that SCC-recursiveness provides an alternative approach to characterize fuzzy extension semantics, offering a deep understanding and better insight into these semantics. Practically, our schema provides a sound and complete algorithm for computing fuzzy extension semantics, which naturally reduces computational efforts when dealing with a large number of SCCs.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes