Dynamic Model Selection Under Time Constraints

这是该奖项为期三年的第一年。本研究解决了在适当的细节水平上建模的问题，目的是在时间或计算资源限制下控制物理或生物系统。研究的目的是开发、实现和测试一种用于实时应用的模型动态选择方法。PI首先通过应用特定于领域的简化假设的组合来开发和组织领域详细（基本）模型的多个简化。他们创建了这些假设的显式表示，并开发了使用这些假设进行动态推理的方法，以找到具有有效假设的最简单模型。根据决策理论的最优性定义，PI检验了具有有效假设的最简单模型是最优的假设。该系统是在人体心肺生理学领域实现的，其控制任务是为呼吸机（机械呼吸装置）寻找设置。该研究将提供在时间关键应用中应用复杂模型的改进方法，使以前难以处理的模型能够在实时应用中使用，并将数值模拟和控制方法与正式决策理论相结合。//

This is the first year of a three year continuing award. This research addresses the problems of modeling at the appropriate level of detail for the purpose of controlling physical or biological systems under time or computation resources constraints. The research is to develop, implement and test a method of dynamic selection of models for use in real-time applications. The PI's first develop and organize multiple simplifications of a detailed (base) model of the domain by applying combinations of domain-specific simplifying assumptions. They create an explicit representation of these assumptions and develop methods to reason dynamically with the assumptions to find the simplest model with valid assumptions. The PI's test the hypothesis that the simplest model with valid assumptions is optimal according to a decision-theoretic definition of optimality. The system is implemented in the domain of human cardiopulmonary physiology, with the control task of finding settings for a ventilator (mechanical breathing device). The research will provide improved methods of applying complex models in time-critical applications, enable the use of previously intractable models in real-time applications, and merge numeric simulation and control methodologies with formal decision theory. //