CAREER: Learning to Cooperate and Compete

职业：学习合作与竞争

• 批准号: 9702672
• 负责人: Sandip Sen
• 金额: $ 25万
• 依托单位: University of Tulsa
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-08-15 至 2003-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9702672&HistoricalAwards=false
• 关键词: CAREER; Learning; Cooperate; Compete

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.

该奖项支持对一系列项目的研究，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，其中每个人的唯一目标是 个人是服务于群体利益， 在纯粹的竞争环境中，个人学会利用竞争对手的弱点。在另一个层面上，该奖项既包括个人主义学习计划，其中每个代理都自己学习，也包括共享学习方法，其中两个或更多代理积极参与相互学习。 为了在合作小组中实现共享学习，小组基础设施是必要的。 在这个奖项下的工作将研究一个组架构和一个灵活的承诺框架，使代理人作出明智的 决定 关于环境动态所必需的可预测性与响应性的权衡。