In Trust We Survive: Emergent Trust Learning
We introduce Emergent Trust Learning (ETL), a lightweight, trust-based control algorithm that can be plugged into existing AI agents. It enables these to reach cooperation in competitive game environments under shared resources. Each agent maintains a compact internal trust state, which modulates memory, exploration, and action selection. ETL requires only individual rewards and local observations and incurs negligible computational and communication overhead. We evaluate ETL in three environments: In a grid-based resource world, trust-based agents reduce conflicts and prevent long-term resource depletion while achieving competitive individual returns. In a hierarchical Tower environment ...
| Authors | Qianpu Chen, Giulio Barbero, Mike Preuss et al. |
|---|---|
| Primary category | cs.MA |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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