Computational argumentation is a field within Artificial Intelligence studying representation, interaction, and computation of arguments. Application domains of approaches to computational argumentation include medicine, accounting, chemistry, and law, as well as other areas that benefiting from automated decision making. With strong roots in non-monotonic reasoning and logic programming, many models of argumentation inherit high computational complexity of argumentative reasoning. Algorithms and systems dealing with argumentative reasoning are a core part of computational argumentation, also witnessed by the popular International Competition on Computational Models of Argumentation (ICCMA). This workshop complements the competition and provides a forum to discuss and present novel approaches and ideas related to algorithmic approaches to all aspects of argumentation. Co-located with the International Conference on Computational Models of Argument (COMMA), a first workshop had been organised in Potsdam (Germany) in 2016, a second one in Warsaw (Poland) in 2018, and a third one virtually in 2020. In this fourth edition, we received nine submissions which were all accepted as regular papers for this volume after a peer-review. Additionally, this volume contains an abstract of the invited talk on “Model Counting, its Relationship to Symbolic Quantitative AI, and a Glimpse into Practical Solving” by Johannes K. Fichte. We thank the Center for Perspicuous Computing (CPEC) and the COMMA 2022 conference organization for their generous support.
Subreviewers Carlo Taticchi Matthias Ko¨nig Tuomo Lehtonen
• Invited Talk: Model Counting, its Relationship to Symbolic Quantitative AI, and a Glimpse into Practical Solving
Johannes Klaus Fichte • Odd or Even: Handling N-lemmas in a Dynamic Argumentation Framework
Kazuko Takahashi • A Sound and Complete Dialogue System for Handling Misunderstandings
Andreas Xydis, Christopher Hampson, Sanjay Modgil, Elizabeth Black • Strong Admissibility, a Tractable Algorithmic Approach
Martin Caminada, Sri Harikrishnan • Computing the Labellings of Higher-Order Abstract Argumentation Frameworks
Sylvie Doutre, Marie-Christine Lagasquie-Schiex • Strategies in Flexible Dispute Derivations for Assumption-Based Argumentation
Martin Diller, Sarah Alice Gaggl, Piotr Gorczyca • Improving Reasoning Eficiency in ASPIC+ with Backwards Chaining and Partial Arguments
Hao Wu, Bruno Yun, Nir Oren • A Labeling Based Backtracking Solver for Abstract Argumentation