HCC: Learning teamwork from observation

HCC：从观察中学习团队合作

• 批准号: 0712869
• 负责人: Ladislau Boloni
• 金额: $ 37.51万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0712869&HistoricalAwards=false
• 关键词: HCC; Learning; teamwork; observation

This project develops algorithms and methods which allow agents to identify and analyze teamwork by the observation of other embodied agents. The goal is to learn from these observations (a) which agents are acting as a team? (b) what collaborative action are they currently executing? and (c) what is the structure of the team, what roles do each of the team members play? The project first creates a corpus of annotated scenarios for training, testing and validation of teamwork learning algorithms. The project then develops a set of algorithms which can detect teamwork between a set of embodied agents by modeling their movement based on reverse force fields. The project also develops algorithms for the robust recognition of known patterns of teamwork using Hidden Markov Models. Finally methods to detect teamwork using the semantic correlation between observations described through semi-formal action descriptions are developed.The research described in this proposal has immediate practical applications. Recognizing teamwork can help robotic teammates integrate in human teams, with immediate applicability in fields such as disaster response. The analysis of the behavior of the team, as well as successful other teams can be an important feedback in training. In homeland security and surveillance applications, recognizing team action in a crowd can help identify terrorist threats.

该项目开发了算法和方法，使代理可以通过观察其他体现的代理来识别和分析团队合作。目的是从这些观察结果中学习（a）哪些代理商是团队？ （b）他们目前正在执行什么协作行动？ （c）团队的结构是什么，每个团队成员扮演什么角色？该项目首先创建了带注释的方案，用于培训，测试和验证团队合作学习算法。然后，该项目开发了一组算法，可以通过基于反向力场对其运动进行建模，可以检测一组体现的代理之间的团队合作。该项目还开发了使用隐藏的马尔可夫模型对团队合作的已知模式的强烈识别的算法。最后，开发了使用通过半正式动作描述描述的观测值之间的语义相关性来检测团队合作的方法。该提案中描述的研究具有直接的实际应用。认识团队合作可以帮助机器人队友在人类团队中融合，并在灾难响应等领域中立即适用。对团队的行为以及成功的其他团队的分析可能是培训中的重要反馈。在国土安全和监视申请中，认识到人群中的团队行动可以帮助确定恐怖威胁。