AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper introduces contrastive explanations for Quantitative Bipolar Argumentation Frameworks (QBAFs), one such formalism, and introduces a general form of contrastive attribution functions (CAFs) and establishes a set of general properties they should satisfy.
This study develops and test a novel theory of debate judgement applicable to all settings where agents engage in debates by providing pros and cons for their opinions therein and indicates argumentation semantics as an ideal candidate for principled judges in debate-driven AI.
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