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Interpreting Neural Networks as Gradual Argumentation Frameworks (Including Proof Appendix).. CoRR, (2020)Delegated updates in epistemic graphs for opponent modelling., , and . Int. J. Approx. Reason., (2019)Polynomial-time Updates of Epistemic States in a Fragment of Probabilistic Epistemic Argumentation (Technical Report)., , and . CoRR, (2019)Interpreting Neural Networks as Quantitative Argumentation Frameworks.. AAAI, page 6463-6470. AAAI Press, (2021)On the Tradeoff Between Correctness and Completeness in Argumentative Explainable AI., , and . ArgXAI@COMMA, volume 3209 of CEUR Workshop Proceedings, CEUR-WS.org, (2022)Learning Gradual Argumentation Frameworks using Meta-heuristics., , , and . ArgML@COMMA, volume 3208 of CEUR Workshop Proceedings, page 96-108. CEUR-WS.org, (2022)Contribution Functions for Quantitative Bipolar Argumentation Graphs: A Principle-based Analysis., , , , and . CoRR, (2024)Robust Knowledge Extraction from Large Language Models using Social Choice Theory., , , , and . CoRR, (2023)Foundations for Solving Classification Problems with Quantitative Abstract Argumentation.. XI-ML@KI, volume 2796 of CEUR Workshop Proceedings, CEUR-WS.org, (2020)Understanding ProbLog as Probabilistic Argumentation., , , and . ICLP, volume 385 of EPTCS, page 183-189. (2023)