S&AS:FND:COLLAB: Planning Coordinated Event Observation for Structured Narratives

人们很容易认出人类事件中发生的戏剧性时刻。事件的戏剧性转折是识别和传达关于事件的有效报道或故事的关键。当自主系统也能识别人类事件的戏剧性（或悲剧性或喜剧性）时，它们将更有效地与人类合作，获取和传达这种叙事。挑战在于如何有效地将这些概念传达给计算机，使人类和自主系统能够有效地协同工作。这项研究研究的是如何指导一组机器人获取视频片段，以制作追踪戏剧性故事弧线的片段。它是对这些系统如何实现人们认为抽象或高级的目标的检验。在这个项目中，指挥机器人团队的程序必须预测可能发生的事件，指导机器人定位以获得所需的镜头，并根据观察到的事件重新规划。这个挑战包含了一个丰富的和以前未研究过的机器人系统问题。这将构成一个独特的展示，机器人能够实现高水平的目标，因为它们以一种新的和不寻常的方式处理数据，将连续和离散的世界观点结合起来。更广泛地说，这项研究将推动计算机如何融合和总结视频流。这两种技能都需要自动生成大纲和编辑视频。这一技术的明显用途包括帮助确保国家安全（用于监控），控制大量在线多媒体内容（用于摘要），以及推进创意产业的应用（用于编辑）。该研究项目还将在一门新的机器人课程中使用这些作品背后的想法，三所院校的学生将在一系列基于竞赛的课堂项目中进行正面交锋。这门课程（除其他地方外，在西班牙裔服务机构教授）将有助于未来STEM劳动力的发展，帮助提高美国的竞争力。该项目通过为自主和机器人系统制定新理论和开发新算法来推进现有知识，重点关注那些很少或没有人为操作干预的系统。该研究为机器人提供了新的数据表示，这些机器人将生活在丰富的环境中，例如那些以不确定、意外和动态变化的环境为特征的环境。该项目的基本思想之一是通过使用计算机语言编译器理论的先前工作，通过递归结构来指定复杂的任务目标。该项目涉及理论工作和演示系统之间的紧密联系。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

People easily recognize the dramatic moments that unfold in human events. Dramatic turns of events are key to recognizing and communicating effective reports or stories about events. Autonomous systems will work more effectively with humans in obtaining and conveying such narrative when they too can recognize what is dramatic (or tragic, or comical) about human events. The challenge is to effectively convey such concepts to a computer in such a way that humans and autonomous systems can effectively work together in this. This research studies how to direct a team of robots to obtain video footage to produce clips that trace a dramatic story arc. It is an examination of how such systems might achieve goals that people consider to be abstract or high-level. Within this project, the programs that command teams of robots must predict likely events, direct the robots to be in position for obtaining the desired footage, and re-plan based on observed events. This challenge encompasses a rich and previously unstudied class of problems for robot systems. It will constitute a unique demonstration of robots that are capable of achieving high-level goals as they process data in forms which combine both continuous and discrete views of the world in a new and unusual way. More broadly, the research will advance how computers can fuse and summarize video streams. Both skills are needed for automatically generating synopses and in editing videos. Obvious places where this is useful include helping secure the nation (for surveillance), taming the deluge of online multimedia content (for summarization), and advancing applications in the creative industries (for editing). The research project will also use the ideas underlying these pieces in a new robotics course with students at three institutions going head-to-head in a series of competition-based class projects. This course (taught, among other places, at a Hispanic-Serving Institution) will contribute to the development of the STEM workforce of the future, helping increase American competitiveness.The project advances current knowledge by formulating new theory and developing novel algorithms for autonomous and robot systems, with a focus on those systems with minimal or no human operator intervention. The research contributes novel data representations for robots that will inhabit rich environments such as those characterized by uncertain, unanticipated, and dynamically changing circumstances. One of the foundational ideas of the project is a means to specify sophisticated mission objectives via a recursive structure using prior work in compiler theory for computer languages. The project involves a strong connection between this theoretical work and demonstrated systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.