Interpreting Neural Networks as Quantitative Argumentation Frameworks
Authors: Nico Potyka6463-6470
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that an interesting class of feed-forward neural networks can be understood as quantitative argumentation frameworks. This connection creates a bridge between research in Formal Argumentation and Machine Learning. We generalize the semantics of feed-forward neural networks to acyclic graphs and study the resulting computational and semantical properties in argumentation graphs. |
| Researcher Affiliation | Academia | Nico Potyka University of Stuttgart, Universit atsstraße 32, 70569 Stuttgart, Germany, nico.potyka@ipvs.uni-stuttgart.de |
| Pseudocode | No | The paper describes mathematical definitions and procedures for MLP-based semantics and continuous MLP-based semantics, but it does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | Yes | The example can be found in the Java library Attractor1 (Potyka 2018b) in the folder examples/divergence. The Java library Attractor (Potyka 2018b) contains an implementation of the Runge-Kutta method RK4 for this purpose. (https://sourceforge.net/projects/attractorproject/) |
| Open Datasets | No | The paper is theoretical and illustrates concepts with examples rather than conducting experiments on conventional datasets with training/testing splits. Therefore, there is no mention of a publicly available dataset for training. |
| Dataset Splits | No | The paper does not use conventional datasets with training, validation, and test splits. Its analysis is primarily theoretical, with illustrative examples rather than empirical evaluation requiring such splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the computations or generate the illustrative examples. |
| Software Dependencies | No | The paper mentions "The Java library Attractor" but does not specify a version number for it or any other software dependencies. |
| Experiment Setup | No | The paper is primarily theoretical and uses illustrative examples to demonstrate concepts (e.g., convergence properties). It does not describe an experimental setup with hyperparameters or training configurations for a machine learning model. |