Many-valued Argumentation, Conditionals and a Probabilistic Semantics for Gradual Argumentation
This work addresses foundational issues in argumentation theory for AI and logic, offering incremental improvements to reasoning capabilities in weighted argumentation graphs.
The paper tackles the problem of enabling conditional reasoning and boolean combinations in gradual argumentation by proposing a many-valued preferential interpretation, and it develops a probabilistic semantics as an extension, with a proof-of-concept using Answer Set Programming for finite cases.
In this paper we propose a general approach to define a many-valued preferential interpretation of gradual argumentation semantics. The approach allows for conditional reasoning over arguments and boolean combination of arguments, with respect to a class of gradual semantics, through the verification of graded (strict or defeasible) implications over a preferential interpretation. As a proof of concept, in the finitely-valued case, an Answer set Programming approach is proposed for conditional reasoning in a many-valued argumentation semantics of weighted argumentation graphs. The paper also develops and discusses a probabilistic semantics for gradual argumentation, which builds on the many-valued conditional semantics.