110 citations · 377 across the 25 of their papers we have counts for
5 papers · 1 filter
Integrating Policy Summaries with Reward Decomposition for Explaining Reinforcement Learning Agents
Yael Septon, Tobias Huber, Elisabeth André +1
Explaining the behavior of reinforcement learning agents operating in sequential decision-making settings is challenging, as their behavior is affected by a dynamic environment and…
On the Generalizability of ECG-based Stress Detection Models
Pooja Prajod, Elisabeth André
Stress is prevalent in many aspects of everyday life including work, healthcare, and social interactions. Many works have studied handcrafted features from various bio-signals that…
Benchmarking Perturbation-based Saliency Maps for Explaining Atari Agents
Tobias Huber, Benedikt Limmer, Elisabeth André
One of the most prominent methods for explaining the behavior of Deep Reinforcement Learning (DRL) agents is the generation of saliency maps that show how much each pixel attribute…
GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial Learning
Silvan Mertes, Tobias Huber, Katharina Weitz +2
With the ongoing rise of machine learning, the need for methods for explaining decisions made by artificial intelligence systems is becoming a more and more important topic. Especi…
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps
Tobias Huber, Katharina Weitz, Elisabeth André +1
With advances in reinforcement learning (RL), agents are now being developed in high-stakes application domains such as healthcare and transportation. Explaining the behavior of th…