A graph convolutional network-based solver for approximating argument acceptability

AFGCN is a software tool for approximate solutions to abstract argumentation using a Graph Convolutional Network (GCN). It addresses the computational complexity of determining argument acceptability across several semantics. The model incorporates deep residual connections, randomized training, and grounded-reasoning features to achieve strong approximation accuracy. The solver predicts acceptability status for credulous and skeptical tasks. Leveraging graph-based learning and an optimized runtime, AFGCN provides an efficient and scalable method for large-scale argumentation frameworks.