GOALI: Statistically Parsimonious Adaptive Dynamic Programming for Minimizing the Environmental Impact of Airport Deicing Activities

目标：统计简约自适应动态规划，最大限度地减少机场除冰活动对环境的影响

• 批准号: 0801802
• 负责人: Victoria Chen
• 金额: $ 30.2万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-06-01 至 2012-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0801802&HistoricalAwards=false
• 关键词: GOALI; Statistically; Parsimonious; Adaptive; Dynamic

NSF Proposal 0801802PI: Chen, VictoriaGOALI: Statistically Parsimonious Adaptive Dynamic Programming for Minimizing the Environmental Impact of Airport Deicing ActivitiesThe objective of this research is to enable computationally-efficient numerical solutions to extremely high-dimensional, nonstationary approximate dynamic programming. This project's Dallas-Fort Worth International Airport deicing activities application involves over 7000 dimensions, while the largest solved to-date is Dr. Chen's air quality application with 524 dimensions. The approach develops a novel and challenging integration of perspectives from statistics, optimization, and reinforcement learning to create efficient new hybrid algorithms.Intellectual Merit:This research's statistical perspective is unique within the approximate dynamic programming community and is the key to parsimony. This project's integrated approach will yield new algorithms for exploration of state and decision spaces, critical for the popular reinforcement learning approach; simultaneous adaptive design and modeling, beneficial to the larger modeling community; and nonconvex optimization of statistical models, useful for general global optimization. The success of this integration will potentially transform the way researchers conduct exploration and modeling in approximate dynamic programming.Broader Impacts:This research is important because it can yield practical methods for complex, uncertain, and dynamic decision-making systems, including environmental policy, health care and medicine, energy distribution and diversity, homeland security, and disaster mitigation. Current solutions use oversimplifications that typically compromise social benefits to satisfy economic constraints, while this project seeks to efficiently represent the true system via an intelligent and parsimonious approach. As a major gateway, the airport must address issues involving many of the above systems, and a long-term collaboration with the airport will yield motivating applications benefiting education, such as dissertations, seminars, and course material.

NSF 提案 0801802PI：Chen，VictoriaGOALI：统计简约自适应动态规划，最大限度地减少机场除冰活动对环境的影响这项研究的目标是为极高维、非平稳近似动态规划提供计算高效的数值解决方案。 该项目的达拉斯沃斯堡国际机场除冰活动应用涉及7000多个维度，而迄今为止解决的最大的是陈博士的空气质量应用，有524个维度。 该方法开发了一种新颖且具有挑战性的统计、优化和强化学习观点的集成，以创建高效的新混合算法。智力优点：这项研究的统计观点在近似动态规划社区中是独一无二的，并且是简约性的关键。 该项目的集成方法将产生用于探索状态和决策空间的新算法，这对于流行的强化学习方法至关重要；同步自适应设计和建模，有利于更大的建模社区；统计模型的非凸优化，对于一般的全局优化很有用。 这种整合的成功将有可能改变研究人员在近似动态规划中进行探索和建模的方式。更广泛的影响：这项研究很重要，因为它可以为复杂、不确定和动态的决策系统提供实用的方法，包括环境政策、健康护理和医疗、能源分配和多样性、国土安全和减灾。 当前的解决方案使用过度简化，通常会牺牲社会效益来满足经济约束，而该项目寻求通过智能和简约的方法有效地代表真实的系统。 作为主要门户，机场必须解决涉及上述许多系统的问题，与机场的长期合作将产生有利于教育的激励性应用，例如论文、研讨会和课程材料。