Learning to Report Unsafe Tasks in a Multi-Agent Game
When a team of agents shares a reward for completed tasks, reporting and stopping unsafe work reduces the team's reward. Random audits that penalize silence can make universal reporting optimal without ensuring that a silent team learns to report. We study this problem in a game where any witness can stop a task by reporting. When a task's k witnesses share a policy and draw actions independently, the derivative of expected reward with respect to the shared silence probability at universal silence counts its benefit k times, while the comparison between universal reporting and silence counts it once. A fully shared policy can therefore be locally attracted to silence even when reporting i...
| Authors | Avyay M. Casheekar, Hariganesh Tangirala |
|---|---|
| Primary category | cs.AI |
| Published | 6 October 2026 |
| Last updated at source | 9 October 2026 |
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