Harnessing Incremental Answer Set Solving for Reasoning in Assumption-Based Argumentation
This work addresses complex reasoning problems in structured argumentation for AI and logic programming communities, but it is incremental as it builds on existing ASP methods for ABA.
The paper tackled reasoning tasks in assumption-based argumentation (ABA) that are presumably hard for the second level of the polynomial hierarchy, such as skeptical reasoning under preferred semantics, by developing algorithms based on incremental answer set programming (ASP) solving. The result showed that these procedures are significantly more effective than previous algorithms, as demonstrated empirically.
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. This paper is under consideration for acceptance in TPLP.