让AI在竞争中学会信任，自动协作不冲突。
In Trust We Survive: Emergent Trust Learning
- 基于内部信任状态动态调节行为，无需全局通信。
- 资源环境中减少冲突，避免长期资源枯竭。
- 适合多智能体博弈、社会困境等场景的智能体设计。
我们提出一种轻量级的信任学习算法（ETL），可嵌入现有AI智能体，在共享资源的竞争环境中实现合作。每个智能体维护一个紧凑的内部信任状态，用于调节记忆、探索与行动选择。ETL仅需个体奖励和局部观测，计算与通信开销极低。在网格状资源世界中，信任机制显著降低冲突，防止长期资源耗尽，同时保持较高的个体收益；在具有强社会困境和随机楼层分配的层级塔环境里，ETL维持高生存率，并能在长期自私行为后恢复合作；在迭代囚徒困境中，该算法泛化至策略元博弈，能与互惠对手持续合作，同时避免被背叛者长期剥削。代码将在发表后公开。
原文摘要 · Abstract (English)
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 with strong social dilemmas and randomised floor assignments, ETL sustains high survival rates and recovers cooperation even after extended phases of enforced greed. In the Iterated Prisoner's Dilemma, the algorithm generalises to a strategic meta-game, maintaining cooperation with reciprocal opponents while avoiding long-term exploitation by defectors. Code will be released upon publication.
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