no code implementations • 16 Jan 2024 • Timotheus Kampik, Nico Potyka, Xiang Yin, Kristijonas Čyras, Francesca Toni
We present a principle-based analysis of contribution functions for quantitative bipolar argumentation graphs that quantify the contribution of one argument to another.
no code implementations • 22 Dec 2023 • Nico Potyka, Yuqicheng Zhu, Yunjie He, Evgeny Kharlamov, Steffen Staab
Large-language models (LLMs) can support a wide range of applications like conversational agents, creative writing or general query answering.
1 code implementation • 11 Dec 2023 • Francesco Leofante, Nico Potyka
Counterfactual explanations shed light on the decisions of black-box models by explaining how an input can be altered to obtain a favourable decision from the model (e. g., when a loan application has been rejected).
no code implementations • 26 Nov 2023 • Hamed Ayoobi, Nico Potyka, Francesca Toni
We propose ProtoArgNet, a novel interpretable deep neural architecture for image classification in the spirit of prototypical-part-learning as found, e. g. in ProtoPNet.
no code implementations • 30 Aug 2023 • Francesca Toni, Nico Potyka, Markus Ulbricht, Pietro Totis
ProbLog is a popular probabilistic logic programming language/tool, widely used for applications requiring to deal with inherent uncertainties in structured domains.
no code implementations • 25 Jul 2023 • Xiang Yin, Nico Potyka, Francesca Toni
Argumentative explainable AI has been advocated by several in recent years, with an increasing interest on explaining the reasoning outcomes of Argumentation Frameworks (AFs).
no code implementations • 21 May 2023 • Markus Ulbricht, Nico Potyka, Anna Rapberger, Francesca Toni
Assumption-based Argumentation (ABA) is a well-known structured argumentation formalism, whereby arguments and attacks between them are drawn from rules, defeasible assumptions and their contraries.
1 code implementation • 23 Jan 2023 • Hamed Ayoobi, Nico Potyka, Francesca Toni
Neural networks (NNs) have various applications in AI, but explaining their decisions remains challenging.
no code implementations • 21 Nov 2022 • Nico Potyka, Xiang Yin, Francesca Toni
Random forests are decision tree ensembles that can be used to solve a variety of machine learning problems.
no code implementations • 19 May 2022 • 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.
1 code implementation • 24 Jan 2022 • Bo Xiong, Nico Potyka, Trung-Kien Tran, Mojtaba Nayyeri, Steffen Staab
Namely, the learned model of BoxEL embedding with loss 0 is a (logical) model of the KB.
1 code implementation • 25 Jun 2021 • Jonathan Spieler, Nico Potyka, Steffen Staab
As a first proof of concept, we propose a genetic algorithm to simultaneously learn the structure of argumentative classification models.
1 code implementation • 6 Jun 2021 • Bo Xiong, Shichao Zhu, Nico Potyka, Shirui Pan, Chuan Zhou, Steffen Staab
Empirical results demonstrate that our method outperforms Riemannian counterparts when embedding graphs of complex topologies.
no code implementations • 10 Dec 2020 • Nico Potyka
However, the connection seems helpful for combining background knowledge in form of sparse argumentation networks with dense neural networks that have been trained for complementary purposes and for learning the parameters of quantitative argumentation frameworks in an end-to-end fashion from data.
no code implementations • 12 Sep 2020 • 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.
no code implementations • 12 Jun 2019 • 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.
no code implementations • 30 Nov 2018 • Nico Potyka
The strength of arguments can be computed based on an initial weight and the strength of attacking and supporting arguments.
no code implementations • 29 Nov 2018 • Nico Potyka
Probabilistic argumentation allows reasoning about argumentation problems in a way that is well-founded by probability theory.
no code implementations • 19 Sep 2018 • Nico Potyka
Semantically, we extend the framework with a Duality property that assures a symmetric impact of attack and support relations.
no code implementations • 10 Jun 2017 • Rafael Peñaloza, Nico Potyka
We present a probabilistic extension of the description logic $\mathcal{ALC}$ for reasoning about statistical knowledge.
no code implementations • 30 Jun 2016 • Rafael Peñaloza, Nico Potyka
A central question for knowledge representation is how to encode and handle uncertain knowledge adequately.