Evaluating XAI Support From A Hierarchical Reinforcement Learning Policy in Human-Agent Collaboration
3.20T1 sourcearXiv cs.HC
Source record
Published by arXiv cs.HC (T1 source). The original is at https://arxiv.org/abs/2608.06381.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryA between-subjects user study (n=38) in the Overcooked-AI benchmark evaluates text versus audio explanations generated from a hierarchical reinforcement learning policy. No significant performance gains were found, but audio explanations significantly reduced participants' working-alliance bond with the agent, suggesting spoken explanations activate partnership expectations a reactive policy cannot meet.
Why it mattersFirst real-time modality comparison in human-agent collaboration flags a counterintuitive design risk: audio explanations can erode trust when the agent's behavior cannot sustain the partnership its voice implies.
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