{"type":"ScholarlyArticle","title":"Instantiations and Computational Aspects of Non-Flat Assumption-based Argumentation","authors":[{"name":"Tuomo Lehtonen","slug":"tuomo-lehtonen","given":"Tuomo","family":"Lehtonen","affiliation":"University of Helsinki , Department of Computer Science"},{"name":"Anna Rapberger","slug":"anna-rapberger","given":"Anna","family":"Rapberger","affiliation":"Imperial College London , Department of Computing"},{"name":"Francesca Toni","slug":"francesca-toni","given":"Francesca","family":"Toni","affiliation":"Imperial College London , Department of Computing"},{"name":"Markus Ulbricht","slug":"markus-ulbricht","given":"Markus","family":"Ulbricht","affiliation":"Leipzig University , ScaDS.AI Dresden / Leipzig"},{"name":"Johannes P. Wallner","slug":"johannes-p-wallner","given":"Johannes P.","family":"Wallner","affiliation":"Graz University of Technology , Institute of Software Technology"}],"abstract":"Most existing computational tools for assumption-based argumentation (ABA) focus on so-called flat frameworks, disregarding the more general case. In this paper, we study an instantiation-based approach for reasoning in possibly non-flat ABA. We make use of a semantics-preserving translation between ABA and bipolar argumentation frameworks (BAFs). By utilizing compilability theory, we establish that the constructed BAFs will in general be of exponential size. To keep the number of arguments and computational cost low, we present three ways of identifying redundant arguments. Moreover, we identify fragments of ABA which admit a poly-sized instantiation. We propose two algorithmic approaches for reasoning in non-flat ABA; the first utilizes the BAF instantiation while the second works directly without constructing arguments. An empirical evaluation shows that the former outperforms the latter on many instances, reflecting the lower complexity of BAF reasoning. This result is in contrast to flat ABA, where direct approaches dominate instantiation-based solvers.","doi":"10.24963/ijcai.2024/383","url":"https://ijcai.org/proceedings/2024/383","pdf":"https://ijcai.org/proceedings/2024/0383.pdf","markdown":"https://ijcai.org/proceedings/2024/383.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/383","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":383,"pages":{"first":3457,"last":3465},"keywords":["Knowledge Representation and Reasoning: KRR: Argumentation","Knowledge Representation and Reasoning: KRR: Computational complexity of reasoning"],"videos":[],"references":[{"position":1,"raw":"[Amgoud et al., 2008] Leila Amgoud, Claudette Cayrol, Marie-Christine Lagasquie-Schiex, and P. 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