AILOJul 17, 2018

Modular Semantics and Characteristics for Bipolar Weighted Argumentation Graphs

arXiv:1807.06685v244 citations
Originality Incremental advance
AI Analysis

This work addresses foundational issues in formal argumentation for AI, offering incremental improvements in semantics analysis for bipolar weighted graphs.

The paper tackles the semantics of bipolar weighted argumentation graphs by introducing a modular framework that separates aggregation and influence functions, proving convergence for all acyclic graphs and divergence for sum-based semantics in cyclic cases, and providing the first semantics that converges for all graphs.

This paper addresses the semantics of weighted argumentation graphs that are bipolar, i.e. contain both attacks and supports for arguments. It builds on previous work by Amgoud, Ben-Naim et. al. We study the various characteristics of acceptability semantics that have been introduced in these works, and introduce the notion of a modular acceptability semantics. A semantics is modular if it cleanly separates aggregation of attacking and supporting arguments (for a given argument $a$) from the computation of their influence on $a$'s initial weight. We show that the various semantics for bipolar argumentation graphs from the literature may be analysed as a composition of an aggregation function with an influence function. Based on this modular framework, we prove general convergence and divergence theorems. We demonstrate that all well-behaved modular acceptability semantics converge for all acyclic graphs and that no sum-based semantics can converge for all graphs. In particular, we show divergence of Euler-based semantics (Amgoud et al.) for certain cyclic graphs. Further, we provide the first semantics for bipolar weighted graphs that converges for all graphs.

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