Extension-ranking Semantics for Abstract Argumentation Preprint
This work addresses the problem of comparing argument sets in abstract argumentation for AI and logic researchers, representing an incremental extension of existing semantics.
The paper introduces a general framework for ranking sets of arguments in abstract argumentation based on plausibility, extending Dung's semantics to allow comparisons between sets. It evaluates this framework by defining principles and adapting existing ranking approaches, resulting in a family of semantics.
In this paper, we present a general framework for ranking sets of arguments in abstract argumentation based on their plausibility of acceptance. We present a generalisation of Dung's extension semantics as extension-ranking semantics, which induce a preorder over the power set of all arguments, allowing us to state that one set is "closer" to being acceptable than another. To evaluate the extension-ranking semantics, we introduce a number of principles that a well-behaved extension-ranking semantics should satisfy. We consider several simple base relations, each of which models a single central aspect of argumentative reasoning. The combination of these base relations provides us with a family of extension-ranking semantics. We also adapt a number of approaches from the literature for ranking extensions to be usable in the context of extension-ranking semantics, and evaluate their behaviour.