{"ID":2882484,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.10976","arxiv_id":"2508.10976","title":"Grounding Rule-Based Argumentation Using Datalog","abstract":"ASPIC+ is one of the main general frameworks for rule-based argumentation for AI. Although first-order rules are commonly used in ASPIC+ examples, most existing approaches to reason over rule-based argumentation only support propositional rules. To enable reasoning over first-order instances, a preliminary grounding step is required. As groundings can lead to an exponential increase in the size of the input theories, intelligent procedures are needed. However, there is a lack of dedicated solutions for ASPIC+. Therefore, we propose an intelligent grounding procedure that keeps the size of the grounding manageable while preserving the correctness of the reasoning process. To this end, we translate the first-order ASPIC+ instance into a Datalog program and query a Datalog engine to obtain ground substitutions to perform the grounding of rules and contraries. Additionally, we propose simplifications specific to the ASPIC+ formalism to avoid grounding of rules that have no influence on the reasoning process. Finally, we performed an empirical evaluation of a prototypical implementation to show scalability.","short_abstract":"ASPIC+ is one of the main general frameworks for rule-based argumentation for AI. Although first-order rules are commonly used in ASPIC+ examples, most existing approaches to reason over rule-based argumentation only support propositional rules. To enable reasoning over first-order instances, a preliminary grounding st...","url_abs":"https://arxiv.org/abs/2508.10976","url_pdf":"https://arxiv.org/pdf/2508.10976v1","authors":"[\"Martin Diller\",\"Sarah Alice Gaggl\",\"Philipp Hanisch\",\"Giuseppina Monterosso\",\"Fritz Rauschenbach\"]","published":"2025-08-14T17:57:32Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[]","has_code":false}