2 citations · 5 across the 9 of their papers we have counts for
11 papers
Explaining Random Forests using Bipolar Argumentation and Markov Networks (Technical Report)
Nico Potyka, Xiang Yin, Francesca Toni
Random forests are decision tree ensembles that can be used to solve a variety of machine learning problems. However, as the number of trees and their individual size can be large,…
Towards a Theory of Faithfulness: Faithful Explanations of Differentiable Classifiers over Continuous Data
Nico Potyka, Xiang Yin, Francesca Toni
There is broad agreement in the literature that explanation methods should be faithful to the model that they explain, but faithfulness remains a rather vague term. We revisit fait…
Learning Gradual Argumentation Frameworks using Genetic Algorithms
Jonathan Spieler, Nico Potyka, Steffen Staab
Gradual argumentation frameworks represent arguments and their relationships in a weighted graph. Their graphical structure and intuitive semantics makes them a potentially interes…
Interpreting Neural Networks as Gradual Argumentation Frameworks (Including Proof Appendix)
Nico Potyka
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 F…
Explainable Automated Reasoning in Law using Probabilistic Epistemic Argumentation
Inga Ibs, Nico Potyka
Applying automated reasoning tools for decision support and analysis in law has the potential to make court decisions more transparent and objective. Since there is often uncertain…
Polynomial-time Updates of Epistemic States in a Fragment of Probabilistic Epistemic Argumentation (Technical Report)
Nico Potyka, Sylwia Polberg, Anthony Hunter
Probabilistic epistemic argumentation allows for reasoning about argumentation problems in a way that is well founded by probability theory. Epistemic states are represented by pro…