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Impact Measures for Gradual Argumentation Semantics

arXiv 2407.08302 ↗First posted 11 Jul 2024cs.AIcited by 5

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Abstract (arXiv)

Argumentation is a formalism allowing to reason with contradictory information by modeling arguments and their interactions. There are now an increasing number of gradual semantics and impact measures that have emerged to facilitate the interpretation of their outcomes. An impact measure assesses, for each argument, the impact of other arguments on its score. In this paper, we refine an existing impact measure from Delobelle and Villata and introduce a new impact measure rooted in Shapley values. We introduce several principles to evaluate those two impact measures w.r.t. some well-known gradual semantics. This comprehensive analysis provides deeper insights into their functionality and desirability.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 8 of its 44 references that are in the map. First posted on arXiv 11 Jul 2024.

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8 of 44 references.

References · 8

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  4. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 299
  5. 2024Contribution Functions for Quantitative Bipolar Argumentation Graphs: A Principle-based Analysiscited by 11
  6. 2023Argument Attribution Explanations in Quantitative Bipolar Argumentation Frameworks (Technical Report)cited by 7
  7. 2015Attacker and Defender Counting Approach for Abstract Argumentationcited by 5
  8. 2021Contrastive Explanations for Argumentation-Based Conclusionscited by 4

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Citing papers per year: 2025: 1; 2026: 4.

5 papers.