2020
IJCAI
IJCAI 2020
Controllability of Control Argumentation Frameworks
Abstract
Control argumentation frameworks (CAFs) allow for modeling uncertainties inherent in various argumentative settings. We establish a complete computational complexity map of the central computational problem of controllability in CAFs for five key semantics. We also develop Boolean satisfiability based counterexample-guided abstraction refinement algorithms and direct encodings of controllability as quantified Boolean formulas, and empirically evaluate their scalability on a range of NP-hard variants of controllability.
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Interdisciplinary Bridge
— Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— argumentation framework
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio