CAREER: Learning to Cooperate and Compete

该奖项支持一系列项目的研究，所有这些项目都解决了个人或团体在包含其他个人和团体的环境中学习提高生产力的问题。多智能体学习场景涵盖的范围从完全合作的群体（每个个体的唯一目标是服务于群体利益）到纯粹竞争的情境（个体学习利用竞争对手的弱点）。在另一个维度上，该奖项既涉及个人学习方案，即每个智能体自己学习，也涉及共享学习方法，即两个或更多智能体积极参与相互学习。为了在合作小组中实现共享学习，一个小组基础设施是必要的。该合同下的工作将研究一个群体架构和一个灵活的承诺框架，使代理能够根据环境动态的需要，对可预测性与响应性之间的权衡做出明智的决定。

The award supports research on a collection of projects, all of which address the problem of individuals or groups learning to be more productive in an environment containing other individuals and groups. The gamut of multi-agent learning scenarios covered ranges from completely cooperative groups, where the sole goal of each individual is to server group interests, to purely competitive situations in which individuals learn to exploit the weaknesses of competitors. On another dimension, the award treats both individualistic learning schemes, where each agent learns by itself, and shared learning approaches, where two or more agents actively participate in learning about each other. To enable shared learning in cooperative groups, a group infrastructure is necessary. Work under this award will study a group architecture and a flexible commitment framework that allows agent to make sensible decision about predictability-versus-responsiveness tradeoffs as necessitated by environmental dynamics.