SoCS: Modeling Agency and Intentions in Dynamic Environments as a Precursor to Efficient Human-Computer Interaction

SoCS：动态环境中的代理和意图建模作为高效人机交互的先驱

• 批准号: 0968538
• 负责人: Brian Scassellati
• 金额: $ 25万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0968538&HistoricalAwards=false
• 关键词: SoCS; Modeling; Agency; Intentions; Dynamic

People recognize dramatic situations and attribute roles and intentions to perceived characters, even when presented with extremely simple cues. As any cartoon viewer can attest, two animated shapes are sufficient to describe a scene involving tender lovers, brutal bullies, tense confrontations and hair-raising escapes. These basic notions of agency and intentionality are foundational to our social perception of the world. They provide the first discriminations between agents and objects, delineate which elements of the world can move with goal-directed purpose, and provide the primitive structure for describing cause and effect. Extensive laboratory experiments have described many of the basic properties that produce these perceptions on controlled stimuli. However there have been only limited attempts to quantify these processes and no attempts to see if these same properties hold on real-world activity patterns.This project models our human ability to perceive agency, intentionality, and goal-directed behavior in dynamic real-world environments. Using off-the-shelf real-time localization systems, the movements of people and objects are recorded as they engage in unstructured activity and staged group games. Drawing on both this empirical data and theories drawn from the psychophysical data, computational models are constructed that quantify, explain, and predict real-world social and goal-directed behavior. The benefits of this work include: (1) modeling tools for use within behavioral studies, (2) a real-world grounding for psychophysical studies, and (3) a computational model of social and intentional behavior that would enhance human-computer and human-robot interfaces.

人们能够识别戏剧性的情境，并将角色和意图归因于感知到的角色，即使是在非常简单的提示下。任何一个卡通观众都可以证明，两个动画图形足以描述一个场景，包括温柔的恋人、残酷的恶霸、紧张的对抗和令人毛骨悚然的逃跑。代理和意向性的这些基本概念是我们对世界的社会感知的基础。它们提供了主体和对象之间的第一个区别，描绘了世界上哪些元素可以以目标导向的目的移动，并提供了描述因果关系的原始结构。大量的实验室实验已经描述了在受控刺激下产生这些感知的许多基本特性。然而，只有有限的尝试量化这些过程，没有尝试看看这些相同的属性是否适用于现实世界的活动模式。这个项目模拟了人类在动态现实环境中感知代理、意向性和目标导向行为的能力。使用现成的实时定位系统，人和物体的运动被记录下来，因为他们参与了非结构化的活动和阶段性的群体游戏。利用这些经验数据和从心理物理数据中得出的理论，构建计算模型来量化、解释和预测现实世界的社会和目标导向行为。这项工作的好处包括：(1)行为研究中使用的建模工具，(2)心理物理学研究的现实世界基础，以及(3)社会和故意行为的计算模型，将增强人机和人机界面。