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arxiv: 1807.06685 · v2 · pith:K6KKHFJHnew · submitted 2018-07-17 · 💻 cs.AI · cs.LO

Modular Semantics and Characteristics for Bipolar Weighted Argumentation Graphs

classification 💻 cs.AI cs.LO
keywords semanticsgraphsmodularbipolaracceptabilityargumentationweightedaggregation
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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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  1. Double Rectified Linear Unit-based Modular Semantics for Quantitative Bipolar Argumentation Framework

    cs.AI 2026-05 unverdicted novelty 6.0

    A double rectified linear unit-based modular semantics for QBAFs yields intuitive acceptability degrees, satisfies rationality postulates, and converges for acyclic plus broader cyclic frameworks.