Aggregative Semantics for Quantitative Bipolar Argumentation Frameworks

2026-03-06

AI Summary

This work addresses the lack of interpretable and flexible gradual semantics in existing quantitative bipolar argumentation frameworks when attacks and supports exhibit asymmetric influence. The paper proposes a novel three-stage aggregation semantics: it first separately aggregates the impacts of attackers and supporters, then integrates these aggregated influences with the intrinsic weight of the argument, thereby preserving complete bipolar information. By treating attacks and supports independently—a first in the field—the approach significantly enhances both semantic interpretability and parametric flexibility. Grounded in formal argumentation theory, the aggregation functions are designed in accordance with established principles of gradual semantics. Empirical validation across 500 diverse instances demonstrates the method’s expressiveness, effectiveness, and strong adaptability across multiple application scenarios.

Technology Category

• Knowledge Representation and Reasoning: Argumentation
• Multiagent Systems: Agreement, Argumentation & Negotiation
• Natural Language Processing: Discourse, Pragmatics & Argument Mining

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Formal argumentation is being used increasingly in artificial intelligence as an effective and understandable way to model potentially conflicting pieces of information, called arguments, and identify so-called acceptable arguments depending on a chosen semantics. This paper deals with the specific context of Quantitative Bipolar Argumentation Frameworks (QBAF), where arguments have intrinsic weights and can attack or support each other. In this context, we introduce a novel family of gradual semantics, called aggregative semantics. In order to deal with situations in which attackers and supporters do not play a symmetric role, and in contrast to modular semantics, we propose to aggregate attackers and supporters separately. This leads to a three-stage computation, which consists in computing a global weight for attackers and another for supporters, before aggregating these two values with the intrinsic weight of the argument. We discuss the properties that the three aggregation functions should satisfy depending on the context, as well as their relationships with the classical principles for gradual semantics. This discussion is supported by various simple examples, as well as a final example on which five hundred aggregative semantics are tested and compared, illustrating the range of possible behaviours with aggregative semantics. Decomposing the computation into three distinct and interpretable steps leads to a more parametrisable computation: it keeps the bipolarity one step further than what is done in the literature, and it leads to more understandable gradual semantics.

Problem

• Quantitative Bipolar Argumentation Frameworks
• gradual semantics
• aggregative semantics
• argumentation
• bipolarity

Innovation

• aggregative semantics
• quantitative bipolar argumentation
• gradual semantics
• asymmetric aggregation
• argumentation frameworks