3 papers
cs.NI2026
Optimizing Reinforcement Learning Training over Digital Twin Enabled Multi-fidelity Networks
Hanzhi Yu, Hasan Farooq, Julien Forgeat +4
In this paper, we investigate a novel digital network twin (DNT) assisted deep learning (DL) model training framework. In particular, we consider a physical network where a base st…
cs.AI2025
Change in Quantitative Bipolar Argumentation: Sufficient, Necessary, and Counterfactual Explanations
Timotheus Kampik, Kristijonas Äyras, José Ruiz Alarcón
This paper presents a formal approach to explaining change of inference in Quantitative Bipolar Argumentation Frameworks (QBAFs). When drawing conclusions from a QBAF and updating…
cs.AI2024
Contribution Functions for Quantitative Bipolar Argumentation Graphs: A Principle-based Analysis
Timotheus Kampik, Nico Potyka, Xiang Yin +2
We present a principle-based analysis of contribution functions for quantitative bipolar argumentation graphs that quantify the contribution of one argument to another. The introdu…