Learning High-level Models of Human Behavior from Low-level Sensor Data

从低级传感器数据学习人类行为的高级模型

• 批准号: 0535126
• 负责人: Henry Kautz
• 金额: $ 29.98万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-12-01 至 2007-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0535126&HistoricalAwards=false
• 关键词: Learning; level; Models; Human; Behavior

Proposal 0535126"Learning High-level Models of Human Behavior from Low-level Sensor Data"PI: Henry KautzUniversity of WashingtonABSTRACTThe goal of this project is to create systems that can interpret and understand day to day human experience. Achieving this goal will support many practical applications with broad social impact, including developing assistive technology for the disabled, creating models of patterns of human interaction for use in the social sciences, and creating new kinds of just-in-time information systems for business and personal use. There are three main themes to this research. First is work in knowledge representation on the structure of goals, plans, and actions. This project is concerned not just with plan synthesis, but also with supporting reasoning about the intentional basis of human action. Second is work on probabilistic reasoning, because any method for interpreting human behavior is necessarily fraught with uncertainty. This work draws upon and extends recent approaches on using model-counting algorithms for probabilistic inference, and methods for combining probability theory with first-order logic. Third is work on ubiquitous sensing: the idea that data from large numbers of simple, inexpensive sensors that directly measure properties of the world can be used to replace or augment complex input modalities such as machine vision or natural language understanding.

提案0535126“从低级传感器数据中学习人类行为的高级模型“PI：亨利考茨华盛顿大学摘要该项目的目标是创建能够解释和理解日常人类经验的系统。实现这一目标将支持许多具有广泛社会影响的实际应用，包括为残疾人开发辅助技术，创建用于社会科学的人类互动模式模型，以及创建用于商业和个人使用的新型即时信息系统。 这项研究有三个主要主题。首先是在目标、计划和行动的结构上进行知识表示的工作。该项目不仅涉及计划综合，而且还涉及对人类行动的意图基础的支持推理。其次是概率推理，因为任何解释人类行为的方法都必然充满不确定性。这项工作借鉴和扩展了最近的方法，使用模型计数算法的概率推理，并结合概率论与一阶逻辑的方法。第三个是无处不在的感知：来自大量简单，廉价的传感器的数据直接测量世界的属性，可以用来取代或增强复杂的输入方式，如机器视觉或自然语言理解。