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
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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.
RANK_REASON The cluster contains two academic papers published on arXiv.