Harnessing Incremental Answer Set Solving for Reasoning in Assumption-Based Argumentation

TL;DR

This paper leverages recent advances in incremental answer set programming to develop efficient algorithms for complex reasoning tasks in assumption-based argumentation, demonstrating significant empirical improvements over previous methods.

Contribution

It introduces counterexample-guided abstraction refinement algorithms using incremental ASP solving for high-level reasoning in ABA, a novel approach in this domain.

Findings

Algorithms outperform previous methods in empirical tests

Effective reasoning for NP-hard tasks in ABA

Significant efficiency gains demonstrated

Abstract

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.