Towards Ranking-based Semantics for Abstract Argumentation using Conditional Logic Semantics
This work addresses a specific problem in computational argumentation for AI researchers, but it appears incremental as it builds on existing semantics with a small extension.
The authors tackled the problem of ranking arguments in Dung-style argumentation frameworks by proposing a new semantics based on conditional logics, which translates frameworks into conditionals and uses nonmonotonic inference to rank arguments, achieving satisfaction of some desirable properties.
We propose a novel ranking-based semantics for Dung-style argumentation frameworks with the help of conditional logics. Using an intuitive translation for an argumentation framework to generate conditionals, we can apply nonmonotonic inference systems to generate a ranking on possible worlds. With this ranking we construct a ranking for our arguments. With a small extension to this ranking-based semantics we already satisfy some desirable properties for a ranking over arguments.