CAREER: Discovering Theoretical Entities

职业：发现理论实体

• 批准号: 0447435
• 负责人: James Oates
• 金额: --
• 依托单位: University of Maryland Baltimore County
• 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: 2005-01-15 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0447435&HistoricalAwards=false
• 关键词: CAREER; Discovering; Theoretical; Entities

The goal of this project is to develop algorithms that will allow robots to discover theoretical entities -- that is, causally relevant features of the environment that cannot be sensed directly. The algorithms developed by the PI will make it possible for robots to expand their understanding and control of the world around them by discovering theoretical entities, such as force and mass, in much the same way as human scientists do. This project also aims to shed light on human cognition, particularly about how we acquire foundational concepts. Bayesian Model Merging will be used to learn a model of the robot's environment. Non-determinism in action outcomes indicates the existence of theoretical entities, and mutual information among posited theoretical entities suggests that a single entity explains observed non-determinism and validates its causal efficacy. This work will be evaluated in three domains: (1) a simulated robot with realistic physics, (2) process control in an aluminum smelting plant, and (3) visual object recognition. This project will make it possible for robots to see beyond the "veil of perception", to overcome limitations of their sensory systems (as humans have), and to better understand, predict, and control their environments. In applying these algorithms to the aluminum smelting process, it will lead to reductions in energy consumption and production of greenhouse gases. Planned educational activities include working with female and minority undergraduate students, teaching a freshman seminar on the scientific method, and development of a new graduate-level course in robotics.

该项目的目标是开发算法，使机器人能够发现理论实体，即无法直接感知的环境的因果相关特征。PI开发的算法将使机器人有可能通过发现理论实体（如力和质量）来扩展它们对周围世界的理解和控制，就像人类科学家所做的一样。这个项目还旨在阐明人类的认知，特别是关于我们如何获得基础概念。贝叶斯模型合并将用于学习机器人环境的模型。行动结果中的非决定论表明理论实体的存在，假设理论实体之间的相互信息表明，单一实体解释了观察到的非决定论并验证了其因果效力。这项工作将在三个领域进行评估：(1)具有逼真物理的模拟机器人，(2)铝冶炼厂的过程控制，以及(3)视觉对象识别。这个项目将使机器人有可能超越“感知的面纱”，克服它们的感官系统的局限性（就像人类一样），更好地理解、预测和控制它们的环境。将这些算法应用于铝冶炼过程，将减少能源消耗和温室气体的产生。计划的教育活动包括与女性和少数民族本科生合作，教授一门关于科学方法的大一研讨会，以及开发一门新的机器人研究生课程。