3 papers
cs.AI2025
Integrating Counterfactual Simulations with Language Models for Explaining Multi-Agent Behaviour
Bálint Gyevnár, Christopher G. Lucas, Stefano V. Albrecht +1
Autonomous multi-agent systems (MAS) are useful for automating complex tasks but raise trust concerns due to risks such as miscoordination or goal misalignment. Explainability is v…
cs.LG2025
Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning
Samuel Garcin, Trevor McInroe, Pablo Samuel Castro +4
Extracting relevant information from a stream of high-dimensional observations is a central challenge for deep reinforcement learning agents. Actor-critic algorithms add further co…
cs.HC2025
People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AI
Balint Gyevnar, Stephanie Droop, Tadeg Quillien +4
It is often argued that effective human-centered explainable artificial intelligence (XAI) should resemble human reasoning. However, empirical investigations of how concepts from c…