ITR: Computational Induction of Scientific Process Models

ITR：科学过程模型的计算归纳

• 批准号: 0326059
• 负责人: Pat Langley
• 金额: $ 265万
• 依托单位: Institute for the Study of Learning and Expertise
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0326059&HistoricalAwards=false
• 关键词: ITR; Computational; Induction; Scientific; Process

This interdisciplinary, interinstitutional collaborative research effort aims to develop a framework that unifies two separate but central themes in information technology -- computational simulation of models to explain important phenomena and computational induction of knowledge from observed regularities in data. Unlike most previous work in machine learning and data mining, the approach emphasizes methods that generate knowledge in established scientific formalisms, incorporate domain knowledge where possible, focus on causal and explanatory models, address induction from observational time-series data, and are embedded in a simulationenvironment which scientists can use for model development.The research revolves around a new class of models that consist ofinteracting quantitative processes and the problem of inducing suchmodels from time-series data. Computational challenges that will beaddressed include reducing overfitting and variance, inducing conditions on processes, handling large, heterogeneous data sets with missingvalues, and scaling to complex models. The resulting algorithms will be included in a trainable simulation environment that lets users construct models manually or induce them from data, then simulate their behavior. Experimental evaluation will involve both Earth Science observationsfrom the Ross Sea and synthetic data.The trainable simulation environment should let Earth scientists searchthe space of candidate models systematically, producing more accurate models in much less time. Moreover, the novel computational methodsshould aid model construction in other fields like systems biology andengineering. Both the environment and sample models will be utilizedin courses and accessible through a Web site. More information isavailable at http://www.isle.org/process.html.

这种跨学科，跨机构的合作研究工作的目的是开发一个框架，统一两个独立的，但在信息技术的中心主题-计算模拟模型来解释重要的现象和计算归纳的知识从观察到的数据。与机器学习和数据挖掘方面的大多数先前工作不同，该方法强调在已建立的科学形式主义中生成知识的方法，在可能的情况下纳入领域知识，专注于因果和解释性模型，解决从观测时间序列数据的归纳，并嵌入到一个模拟环境中，科学家可以使用该环境进行模型开发。该研究围绕着一类新的模型，定量过程和问题的诱导suchmodels从时间序列数据.将解决的计算挑战包括减少过拟合和方差，诱导过程条件，处理具有缺失值的大型异构数据集，以及扩展到复杂模型。由此产生的算法将包含在一个可训练的模拟环境中，让用户手动构建模型或从数据中诱导模型，然后模拟它们的行为。实验评估将涉及罗斯海的地球科学观测和合成数据。可训练的模拟环境应该让地球科学家系统地搜索候选模型的空间，在更短的时间内产生更准确的模型。此外，新的计算方法应该有助于系统生物学和工程学等其他领域的模型构建。环境和样本模型都将在课程中使用，并可通过网站访问。更多信息请访问http://www.isle.org/process.html。