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建议0801802 PI：陈，陈戈：统计吝啬自适应动态规划最大限度地减少机场除冰活动的环境影响本研究的目的是使计算效率高的数值解非常高维，非平稳近似动态规划。 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整合，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界中是独一无二的，是简约的关键。 该项目的综合方法将产生用于探索状态和决策空间的新算法，这对流行的强化学习方法至关重要;同时进行自适应设计和建模，有利于更大的建模社区;以及统计模型的非凸优化，对一般全局优化有用。 这种整合的成功将可能改变研究人员进行探索和建模的方式在近似动态programming.Broader影响：这项研究是重要的，因为它可以产生复杂的，不确定的，动态的决策系统，包括环境政策，医疗保健和医药，能源分配和多样性，国土安全和减灾的实用方法。 目前的解决方案使用过度简化，通常会损害社会效益，以满足经济约束，而这个项目旨在通过一个智能和简约的方法，有效地代表真正的系统。 作为一个主要的门户，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益于教育的激励性应用，如论文，研讨会和课程材料。