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cs.AI2024
Applying Attribution Explanations in Truth-Discovery Quantitative Bipolar Argumentation Frameworks
Xiang Yin, Nico Potyka, Francesca Toni
Explaining the strength of arguments under gradual semantics is receiving increasing attention. For example, various studies in the literature offer explanations by computing the a…
cs.AI2024
CE-QArg: Counterfactual Explanations for Quantitative Bipolar Argumentation Frameworks (Technical Report)
Xiang Yin, Nico Potyka, Francesca Toni
There is a growing interest in understanding arguments' strength in Quantitative Bipolar Argumentation Frameworks (QBAFs). Most existing studies focus on attribution-based methods…
cs.AI2024
Contribution Functions for Quantitative Bipolar Argumentation Graphs: A Principle-based Analysis
Timotheus Kampik, Nico Potyka, Xiang Yin +2
We present a principle-based analysis of contribution functions for quantitative bipolar argumentation graphs that quantify the contribution of one argument to another. The introdu…