Jonathan Spieler

Software Engineer

Particularly interested in Machine Learning, Reinforcement Learning, and Software Design.

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Publications

My published works

Interpretable Machine Learning with Gradual Argumentation Frameworks

Jonathan Spieler, Nico Potyka, Steffen StaabComputational Models of Argument - Proceedings of COMMA 2022Read Abstract

Interpretable Machine Learning with Gradual Argumentation Frameworks

Gradual argumentation frameworks represent arguments and their relationships in a weighted graph. Their mechanics are closely related to neural networks, which are considered as black-box models due to their dense structure with millions of neurons. Recent work tried making them human-understandable by trying to learn parameters that can be well approximated by decision trees. However, the tree remains just an approximation, which leaves the question how faithful it really captures the actual mechanics of the neural network. To circumvent the problem, we tailor ideas for learning sparse neural networks to the discrete structure of gradual argumentation frameworks to learn sparse neural networks that can be directly interpreted as gradual argumentation frameworks. We present a genetic algorithm and evaluate it on benchmarks from the UCI machine learning repository. Our implementation is publicly available on Github https://github.com/jspieler/QBAF-Learning.

Learning Gradual Argumentation Frameworks using Meta-heuristics

Nico Potyka, Mohamad Bazo, Jonathan Spieler, Steffen StaabProceedings of the 1st Workshop on Argumentation & Machine Learning co-located with 9th International Conference on Computational Models of Argument (COMMA 2022), Cardiff, Wales, September 13th, 2022Read Abstract

Learning Gradual Argumentation Frameworks using Meta-heuristics

Gradual argumentation frameworks represent arguments and their relationships in a weighted graph. Their mechanics are closely related to neural networks, which are considered as black-box models due to their dense structure with millions of neurons. Recent work tried making them human-understandable by trying to learn parameters that can be well approximated by decision trees. However, the tree remains just an approximation, which leaves the question how faithful it really captures the actual mechanics of the neural network. To circumvent the problem, structure learning ideas can be tailored to the discrete structure of gradual argumentation frameworks to learn sparse neural networks that can be directly interpreted as gradual argumentation frameworks. We discuss the learning problem, sketch a genetic and a particle swarm optimization algorithm to solve the problem and show first results on data sets from the UCI machine learning repository.

Learning Gradual Argumentation Frameworks using Genetic Algorithms

Jonathan Spieler, Nico Potyka, Steffen StaabarXiv:2106.13585Read Abstract

Learning Gradual Argumentation Frameworks using Genetic Algorithms

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.