Controllability of Control Argumentation Frameworks
Authors: Andreas Niskanen, Daniel Neugebauer, Matti Järvisalo
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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 NPhard variants of controllability. |
| Researcher Affiliation | Academia | 1HIIT, Department of Computer Science, University of Helsinki, Finland 2Institut f ur Informatik, Heinrich-Heine Universit at D usseldorf, Germany |
| Pseudocode | Yes | Algorithm 1 CEGAR for skeptical controllability Input: CAF C = (F, C, U), target T AF , σ {com, stb}... Algorithm 2 CEGAR for credulous controllability Input: CAF C = (F, C, U), target T AF , σ {adm, stb}. |
| Open Source Code | Yes | The implementation, benchmarks, and runtime data are available online. |
| Open Datasets | Yes | We generated CAFs from the 2019 ICCMA competition AFs (http://argumentationcompetition.org/2019/iccma-instances.tar.gz) as follows. |
| Dataset Splits | No | The paper describes how the CAFs were generated from existing AFs by introducing probabilistic parameters (p C, p U) for control arguments and uncertain parts. However, it does not specify a division of these generated instances into distinct training, validation, and test sets. The generated instances are used directly for evaluation. |
| Hardware Specification | Yes | The experiments were run on Intel Xeon E5-2680 v4 2.4-GHz, 256-GB nodes with Cent OS 7 under a per-instance 900-s time and 64-GB memory limit. |
| Software Dependencies | Yes | We used the QBF solver CAQE 4.0.0 [Tentrup, 2019] with the Bloqqer [Heule et al., 2015] preprocessor and the flag --qdo to obtain assignments corresponding to control configurations. For CEGAR we used the Glucose 4.1 SAT solver [Audemard and Simon, 2018]. |
| Experiment Setup | Yes | For each p C {0.05, 0.1, 0.15, 0.2}, each non-query argument is a control argument with probability p C. For each p U {0, 0.05, 0.1, 0.15, 0.2}, each argument (apart from control and query arguments) is uncertain with probability p U. Each attack is uncertain with probability p U/2 unless the source or the target is a control argument, and has uncertain direction with probability p U/2 unless the reverse direction is already a fixed or an uncertain attack. The rest of the arguments and attacks remain fixed. This yielded a total of 6520 CAFs, out of which 1304 are simplified CAFs with no uncertain part. The experiments were run... under a per-instance 900-s time and 64-GB memory limit. |