HCC: Learning teamwork from observation

该项目开发了算法和方法，允许代理通过观察其他嵌入代理来识别和分析团队合作。目标是从这些观察中学习(a)哪些代理作为一个团队行动？(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？该项目首先创建了一个带注释的场景语料库，用于团队学习算法的训练、测试和验证。然后，该项目开发了一套算法，可以通过基于反向力场的运动建模来检测一组具身代理之间的团队合作。该项目还开发了使用隐马尔可夫模型对已知团队模式进行鲁棒识别的算法。最后，提出了利用半形式化动作描述所描述的观察结果之间的语义相关性来检测团队合作的方法。本提案所描述的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，在灾难响应等领域具有直接的适用性。分析团队的行为，以及成功的其他团队可以作为训练中的重要反馈。在国土安全和监视应用中，识别人群中的团队行动可以帮助识别恐怖主义威胁。

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.