158 citations · 183 across the 14 of their papers we have counts for
15 papers
Leveraging LLMs for Collaborative Ontology Engineering in Parkinson Disease Monitoring and Alerting
Georgios Bouchouras, Dimitrios Doumanas, Andreas Soularidis +2
This paper explores the integration of Large Language Models (LLMs) in the engineering of a Parkinson's Disease (PD) monitoring and alerting ontology through four key methodologies…
Learning safe, constrained policies via imitation learning: Connection to Probabilistic Inference and a Naive Algorithm
George Papadopoulos, George A. Vouros
This article introduces an imitation learning method for learning maximum entropy policies that comply with constraints demonstrated by expert trajectories executing a task. The fo…
Explainable Deep Reinforcement Learning: State of the Art and Challenges
George A. Vouros
Interpretability, explainability and transparency are key issues to introducing Artificial Intelligence methods in many critical domains: This is important due to ethical concerns…
XDQN: Inherently Interpretable DQN through Mimicking
Andreas Kontogiannis, George Vouros
Although deep reinforcement learning (DRL) methods have been successfully applied in challenging tasks, their application in real-world operational settings is challenged by method…
Automating the resolution of flight conflicts: Deep reinforcement learning in service of air traffic controllers
George Vouros, George Papadopoulos, Alevizos Bastas +2
Dense and complex air traffic scenarios require higher levels of automation than those exhibited by tactical conflict detection and resolution (CD\&R) tools that air traffic contro…
Data-driven prediction of Air Traffic Controllers reactions to resolving conflicts
Alevizos Bastas, George A. Vouros
With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the Air Traffic Management domain, in this paper we propose deep learning techniques (DL) th…