Graph Convolutional Networks and Graph Attention Networks for Approximating Arguments Acceptability -- Technical Report
<|reference_start|>Graph Convolutional Networks and Graph Attention Networks for Approximating Arguments Acceptability -- Technical Report: Various approaches have been proposed for providing efficient computational approaches for abstract argumentation. Among them, neural networks have permitted to solve various decision problems, notably related to arguments (credulous or skeptical) acceptability. In this work, we push further this study in various ways. First, relying on the state-of-the-art approach AFGCN, we show how we can improve the performances of the Graph Convolutional Networks (GCNs) regarding both runtime and accuracy. Then, we show that it is possible to improve even more the efficiency of the approach by modifying the architecture of the network, using Graph Attention Networks (GATs) instead.<|reference_end|>
@article{cibier2024graph,
    title={Graph Convolutional Networks and Graph Attention Networks for
  Approximating Arguments Acceptability -- Technical Report},
    author={Paul Cibier and Jean-Guy Mailly},
    journal={arXiv preprint arXiv:2404.18672},
    year={2024},
    doi={10.3233/FAIA240307},
    archivePrefix={arXiv},
    eprint={2404.18672},
    primaryClass={cs.AI}
}