Evaluating XAI Support From A Hierarchical Reinforcement Learning Policy in Human-Agent Collaboration
Explainable AI (XAI) has shown promise for human-agent collaboration, yet results rely on hand-crafted policies in custom environments, limiting generalizability to state-of-the-art teaming research. We provide the first systematic evaluation of XAI support generated from an intrinsically explainable learned policy in an established benchmark. Using the Hierarchical Ad Hoc Agents (HA$^2$) architecture in Overcooked-AI, we generate real-time explanations from hierarchical subtask selections, delivered through text or audio via a novel trigger-based system. Our between-subjects experiment (n=38) found no significant performance effects, though participants with explanations showed trends to...
| Authors | Mateus Levi Simões Fernandes, Alberto Sardinha |
|---|---|
| Primary category | cs.HC |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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