Abstract
AbstractAssumption-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.
Highlights
Argumentation, and in particular the study of computational models of argument, constitutes a core research area in artificial intelligence, and knowledge representation and nonmonotonic reasoning in particular (Baroni et al . 2018)
These central structured argumentation formalisms have found applications for example, in decision making in a multi-agent context (Fan et al . 2014), game theory (Fan and Toni 2016), and in choosing treatment recommendations based on clinical guidelines and preferential information given by patients (Cyras and Oliveira 2019)
Following successful schemes for the same reasoning task on abstract argumentation frameworks (AFs) (Cerutti et al . 2018), we present Algorithm 1 for deciding skeptical acceptance of sentences in an assumption-based argumentation (ABA) framework
Summary
Argumentation, and in particular the study of computational models of argument, constitutes a core research area in artificial intelligence, and knowledge representation and nonmonotonic reasoning in particular (Baroni et al . 2018). 2018) and its extension, ABA+, equipped with preferences (Cyras and Toni 2016a) These central structured argumentation formalisms have found applications for example, in decision making in a multi-agent context 2017; 2021a), in terms of scalability arguably the currently most efficient practical approach is based on encoding ABA reasoning tasks declaratively using answer set programming (ASP) (Gelfond and Lifschitz 1988; Niemela 1999), and invoking off-the-shelf ASP solvers for the reasoning part 2017) for enumerating admissible and complete assumption sets in ABA+, our approach provides significant performance improvements in practice and allows for directly reasoning about credulous acceptance in ABA+. Our implementation is available at https://bitbucket. org/coreo-group/aspforaba
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