arXiv (All)
open access
Learning to Report Unsafe Tasks in a Multi-Agent Game
When agents share a reward for completed tasks, reporting unsafe work can reduce the reporter's reward by stopping a task. Audits can make reporting optimal without ensuring that further training teaches a silent team to report. We study this learning problem in a game where any witness can stop a t
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- SafeDiSTL: Decomposing Joint Constraints for Safe Multi-Agent Reinforcement Learning#1298233
- SafeDiSTL: Decomposing Joint Constraints for Safe Multi-Agent Reinforcement Learning#1291820
- Task Decomposition with Multi-Agent Systems#642208
- Limit-sure Winning in Team Games without Shared Randomness#1297921
- Deny Without Disabling: Authorization-Paired Evaluation and Control for Multi-Agent Systems#1154994
Record · ID 1233041
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive