The paper Controllability of Control Argumentation Frameworks, authored by Andreas Niskanen and Matti Järvisalo together with Daniel Neugebauer (University of Düsseldorf, Germany), has been accepted for publication in the proceedings of the 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI 2020), the top general AI conference word-wide.
The paper focuses on control argumentation frameworks (CAFs) which allow for modeling uncertainties inherent in various argumentative settings. A complete computational complexity map of the central computational problem of controllability in CAFs for five key semantics is established, and Boolean satisfiability based counterexample-guided abstraction refinement algorithms and direct encodings of controllability as quantified Boolean formulas are developed and em- pirically evaluated.