Controllability of Control Argumentation Frameworks
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 1855-1861. https://doi.org/10.24963/ijcai.2020/257
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.
Knowledge Representation and Reasoning: Computational Models of Argument
Knowledge Representation and Reasoning: Computational Complexity of Reasoning
Constraints and SAT: SAT: : Solvers and Applications