Splitting Argumentation Frameworks with Collective Attacks and Supports
Two new papers explore advanced splitting techniques for argumentation frameworks to improve computational efficiency. The first paper focuses on Assumption-Based Argumentation Frameworks (ABAFs), proposing splitting on the knowledge base rather than its graph instantiation to handle computational complexity. The second paper introduces novel splitting methods for bipolar set-based argumentation frameworks (BSAFs), which incorporate both collective attacks and supports, generalizing existing techniques. AI
IMPACT These papers introduce novel computational techniques for argumentation frameworks, potentially improving the efficiency of AI systems that rely on logical reasoning and debate modeling.