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.

该项目开发算法和方法，允许代理通过观察其他具体代理来识别和分析团队合作。我们的目标是从这些观察中学习（a）哪些代理人作为一个团队？(b)他们目前正在采取什么合作行动？及（c）小组的结构为何，每个成员担当甚么角色？该项目首先创建了一个注释场景语料库，用于训练，测试和验证团队学习算法。然后，该项目开发了一套算法，可以通过基于反向力场对一组具体代理的运动进行建模来检测它们之间的团队合作。该项目还开发了算法，用于使用隐马尔可夫模型对已知的团队合作模式进行鲁棒识别。最后，通过半形式化的动作描述，提出了利用观测之间的语义相关性来检测团队合作的方法，该研究具有直接的实际应用价值。认识到团队合作可以帮助机器人队友融入人类团队，并立即适用于灾难应对等领域。团队的行为分析，以及成功的其他团队可以在培训中的重要反馈。在国土安全和监控应用中，识别人群中的团队行动有助于识别恐怖威胁。