The article Harnessing Incremental Answer Set Solving for Reasoning in Assumption-Based Argumentation, authored by Tuomo Lehtonen and Matti Järvisalo of the Constraint Reasoning and Optimization group together with Johannes P. Wallner (TU Graz, Austria), has been accepted for publications in the journal Theory and Practice of Logic Programming. The work will be presented at ICLP 2021, 37th International Conference on Logic Programming, in September.
Assumption-based argumentation (ABA) is a central structured argumentation formalism. As shown recently, answer set programming (ASP) enables efficiently solving NP-hard reasoning tasks of ABA in practice, in particular in the commonly studied logic programming fragment of ABA. In this work, we harness recent advances in incremental ASP solving for developing effective algorithms for reasoning tasks in the logic programming fragment of ABA that are presumably hard for the second level of the polynomial hierarchy, including skeptical reasoning under preferred semantics as well as preferential reasoning. In particular, we develop non-trivial counterexample-guided abstraction refinement procedures based on incremental ASP solving for these tasks. We also show empirically that the procedures are significantly more effective than previously proposed algorithms for the tasks.