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Aggregative Semantics for Quantitative Bipolar Argumentation Frameworks

arXiv:2603.06067v1 Announce Type: new 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

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Yann Munro, Isabelle Bloch, Marie-Jeanne Lesot · · 1 min read · 16 views
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arXiv:2603.06067v1 Announce Type: new 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.

Executive Summary

This article proposes a novel family of gradual semantics, called aggregative semantics, for Quantitative Bipolar Argumentation Frameworks (QBAF). The authors address the limitation of modular semantics by aggregating attackers and supporters separately, leading to a three-stage computation. The properties of the three aggregation functions and their relationships with classical principles for gradual semantics are discussed. The authors provide various examples, including a comprehensive comparison of five hundred aggregative semantics. This approach enhances the parametrisability and interpretability of QBAF, keeping bipolarity one step further than existing literature. The findings have significant implications for artificial intelligence applications, particularly in modeling conflicting information and identifying acceptable arguments.

Key Points

  • ▸ Proposed aggregative semantics for QBAF, addressing limitations of modular semantics
  • ▸ Three-stage computation for aggregating attackers and supporters separately
  • ▸ Discussion of properties and relationships with classical principles for gradual semantics

Merits

Strength

The proposed aggregative semantics offer a more parametrisable and interpretable computation, enhancing the bipolarity of QBAF. The three-stage computation provides a clearer understanding of how attackers and supporters are aggregated.

Strength

The authors provide a comprehensive comparison of five hundred aggregative semantics, illustrating the range of possible behaviours and supporting the validity of the approach.

Demerits

Limitation

The proposed semantics may be computationally intensive due to the three-stage computation, which could impact performance in large-scale applications.

Limitation

The paper assumes a fixed set of aggregation functions, which might not be adaptable to diverse real-world scenarios.

Expert Commentary

This article makes a significant contribution to the field of formal argumentation in artificial intelligence. The proposed aggregative semantics offer a more parametrisable and interpretable computation, addressing the limitations of modular semantics. The authors' discussion of properties and relationships with classical principles provides a solid foundation for future research. However, the proposed semantics may be computationally intensive, and the fixed set of aggregation functions might not be adaptable to diverse real-world scenarios. Nevertheless, this paper opens up new avenues for research and has significant implications for AI applications.

Recommendations

  • ✓ Future research should focus on developing more efficient algorithms for computing aggregative semantics in large-scale applications.
  • ✓ The authors should investigate the adaptability of the proposed semantics to diverse real-world scenarios by considering different aggregation functions and contexts.

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