In scenarios where a single player cannot control other players, cooperative AI is a recent technology that takes advantage of deep learning to assess whether cooperation might occur. One main difficulty of this approach is that it requires a certain level of consensus on the protocol (actions and rules), at least from a majority of players. In our work, we study the simulations performed on the cooperative AI tool proposed in the context of AI for Global Climate Cooperation (AI4GCC) competition. We experimented simulations with and without the AI4GCC default negotiation, including with regions configured slightly differently in terms of labor and/or technology growth. These first results showed that the AI4GCC framework offers a promising cooperative framework to experiment with global warming mitigation. We also propose future work to strengthen this framework.

翻译：在单一参与者无法控制其他参与者的场景中，合作型人工智能是一种利用深度学习评估合作可能性的新兴技术。该方法的主要难点在于需要至少多数参与者就协议（行为与规则）达成一定程度的共识。在本研究中，我们分析了在全球气候合作人工智能（AI4GCC）竞赛框架下提出的合作型AI工具所执行的模拟实验。我们分别开展了包含和不包含AI4GCC默认协商机制的模拟实验，并特别针对部分区域在劳动力和/或技术增长率方面进行了细微调整。初步结果表明，AI4GCC框架为全球变暖减缓研究提供了富有前景的合作实验平台。同时，我们提出了未来强化该框架的研究方向。