Relevance in Structured Argumentation
This work addresses theoretical foundations for non-monotonic reasoning in AI, focusing on structured argumentation, but appears incremental as it builds on existing criteria from relevance logic and argumentation theory.
The paper investigates properties of structured argumentation systems that ensure relevance desiderata, such as robustness under adding irrelevant information, using syntactic and semantic criteria.
We study properties related to relevance in non-monotonic consequence relations obtained by systems of structured argumentation. Relevance desiderata concern the robustness of a consequence relation under the addition of irrelevant information. For an account of what (ir)relevance amounts to we use syntactic and semantic considerations. Syntactic criteria have been proposed in the domain of relevance logic and were recently used in argumentation theory under the names of non-interference and crash-resistance. The basic idea is that the conclusions of a given argumentative theory should be robust under adding information that shares no propositional variables with the original database. Some semantic relevance criteria are known from non-monotonic logic. For instance, cautious monotony states that if we obtain certain conclusions from an argumentation theory, we may expect to still obtain the same conclusions if we add some of them to the given database. In this paper we investigate properties of structured argumentation systems that warrant relevance desiderata.