Enforcement Heuristics for Argumentation with Deep Reinforcement Learning
Authors: Dennis Craandijk, Floris Bex5573-5581
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that our Graph Neural Network (GNN) architecture EGNN can learn a near optimal enforcement heuristic for all common argument-fixed enforcement problems, including problems for which no other (symbolic) solvers exist. We demonstrate that EGNN outperforms other GNN baselines and on enforcement problems with high computational complexity performs better than state-of-the-art symbolic solvers with respect to efficiency. ... Section discusses the experimental setup (data, training parameters), and Section discusses the results. |
| Researcher Affiliation | Collaboration | Dennis Craandijk1,2 and Floris Bex2,3 1 National Police-lab AI, Netherlands Police 2 Department Information and Computing Sciences, Utrecht University 3 Tilburg Institute for Law, Technology and Society, Tilburg University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We publish our code at https://github.com/Dennis Craandijk/DLAbstract-Argumentation. |
| Open Datasets | Yes | We sample AFs uniformly from all AF families implemented in the following generators from ICCMA (Gaggl et al. 2020): AFBench Gen2, AFGen Benchmark Generator, Grounded Generator, Scc Generator, Stable Generator. ... We generate training instances with |A| from (3, 4, 5, ..., 9) and 1000 validation instances containing |A| = 10 arguments to train the network. |
| Dataset Splits | Yes | We generate training instances with |A| from (3, 4, 5, ..., 9) and 1000 validation instances containing |A| = 10 arguments to train the network. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or memory used for experiments. |
| Software Dependencies | No | The paper mentions external tools like 'ยต-toksia solver (Niskanen and J arvisalo 2020a)', 'Pakota', and 'Maadoita', but does not provide specific version numbers for these or other software dependencies like deep learning frameworks (e.g., Python, PyTorch versions). |
| Experiment Setup | No | The 'Experimental Setup' section describes the data generation and models used but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations. |