The Capabilities and Limitations of Atomated Discovery

自动化发现的能力和局限性

• 批准号: 9732692
• 负责人: Carl Smith
• 金额: $ 19.3万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-07-01 至 2001-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9732692&HistoricalAwards=false
• 关键词: Capabilities; Limitations; Atomated; Discovery

The models used in inductive inference have their roots in the models used by the philosophers of science who were discussing the scientific method. The goal there, and in prior work in learning theory, was to come up with an explanation of the phenomenon under consideration. However, scientists rarely work directly for the grand goal of a complete explanation, seeking rather the more modest goal of finding features and facts about the observed data. In like manner, this project pursues several modifications to the traditional models used in inductive inference so as to study the pursuit of more modest scientific goals. This study is particularly relevant to contemporary science as automated data generation techniques produce sufficient volumes of data to overwhelm the analysis abilities of humans. The goal of our work is to illuminate precisely what can and cannot be accomplished by automatic data analysis algorithms.

归纳推理中使用的模型起源于科学哲学家讨论科学方法时所使用的模型。这一研究的目标，以及之前学习理论的研究，都是要对所考虑的现象作出解释。然而，科学家们很少直接为一个完整的解释的宏伟目标而努力，而是寻求找到观测数据的特征和事实的更温和的目标。同样，本项目对归纳推理中使用的传统模型进行了一些修改，以研究追求更适度的科学目标。这项研究与当代科学特别相关，因为自动化数据生成技术产生的数据量足以压倒人类的分析能力。我们工作的目标是精确地阐明自动数据分析算法能完成什么和不能完成什么。