5 citations · 8 across the 4 of their papers we have counts for
4 papers
Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models
Senne Deproost, Denis Steckelmacher, Ann Nowé
Despite many successful attempts at explaining Deep Reinforcement Learning policies using distillation, it remains difficult to balance the performance-interpretability trade-off a…
A three-Level Framework for LLM-Enhanced eXplainable AI: From technical explanations to natural language
Marilyn Bello, Rafael Bello, Maria-Matilde García +3
The growing application of artificial intelligence in sensitive domains has intensified the demand for systems that are not only accurate but also explainable and trustworthy. Alth…
Explainable AI Based Diagnosis of Poisoning Attacks in Evolutionary Swarms
Mehrdad Asadi, Roxana Rădulescu, Ann Nowé
Swarming systems, such as for example multi-drone networks, excel at cooperative tasks like monitoring, surveillance, or disaster assistance in critical environments, where autonom…
Human-Readable Programs as Actors of Reinforcement Learning Agents Using Critic-Moderated Evolution
Senne Deproost, Denis Steckelmacher, Ann Nowé
With Deep Reinforcement Learning (DRL) being increasingly considered for the control of real-world systems, the lack of transparency of the neural network at the core of RL becomes…