# Learning Gradual Argumentation Frameworks using Genetic Algorithms

arXiv: https://arxiv.org/abs/2106.13585 · HTML: https://mlsanity.com/paper/2106.13585/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 64 references that are in the map.
- First posted on arXiv 25 Jun 2021.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://mlsanity.com/paper/1511.00363/) (2015)
- [Speeding up Convolutional Neural Networks with Low Rank Expansions](https://mlsanity.com/paper/1405.3866/) (2014)
- [Dynamic Network Surgery for Efficient DNNs](https://mlsanity.com/paper/1608.04493/) (2016)
- [Predicting Parameters in Deep Learning](https://mlsanity.com/paper/1306.0543/) (2013)
- [Bayesian Compression for Deep Learning](https://mlsanity.com/paper/1705.08665/) (2017)
- [Convolutional neural networks with low-rank regularization](https://mlsanity.com/paper/1511.06067/) (2015)
- [Neural Networks with Few Multiplications](https://mlsanity.com/paper/1510.03009/) (2015)
- [Sparse evolutionary Deep Learning with over one million artificial neurons on commodity hardware](https://mlsanity.com/paper/1901.09181/) (2019)
- [Modular Semantics and Characteristics for Bipolar Weighted Argumentation Graphs](https://mlsanity.com/paper/1807.06685/) (2018)
- [Deep Learning for Abstract Argumentation Semantics](https://mlsanity.com/paper/2007.07629/) (2020)
- [Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks](https://mlsanity.com/paper/1611.01427/) (2016)
- [A Tutorial for Weighted Bipolar Argumentation with Continuous Dynamical Systems and the Java Library Attractor](https://mlsanity.com/paper/1811.12787/) (2018)
- [Characterizing Sparse Connectivity Patterns in Neural Networks](https://mlsanity.com/paper/1711.02131/) (2017)
- [Deep Adaptive Network: An Efficient Deep Neural Network with Sparse Binary Connections](https://mlsanity.com/paper/1604.06154/) (2016)

## Abstract (arXiv)

Gradual argumentation frameworks represent arguments and their relationships in a weighted graph. Their graphical structure and intuitive semantics makes them a potentially interesting tool for interpretable machine learning. It has been noted recently that their mechanics are closely related to neural networks, which allows learning their weights from data by standard deep learning frameworks. As a first proof of concept, we propose a genetic algorithm to simultaneously learn the structure of argumentative classification models. To obtain a well interpretable model, the fitness function balances sparseness and accuracy of the classifier. We discuss our algorithm and present first experimental results on standard benchmarks from the UCI machine learning repository. Our prototype learns argumentative classification models that are comparable to decision trees in terms of learning performance and interpretability.