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This paper introduces contrastive explanations for Quantitative Bipolar Argumentation Frameworks, explaining differences between two topic arguments to enhance AI explainability, with applications in healthcare and bias identification.
Introduces context-dependent argumentation frameworks (CDAFs) that model how an agent can strategically influence which attacks succeed by choosing a context, enabling manipulation scenarios not possible in value-based argumentation. Defines the ACTIVATION-MANIPULATION decision problem and provides baseline complexity bounds.