SGER: Neural-Network-Based Adaptive Dynamic Programming for Maximizing Survival Probabilities

提案编号：0841055提案标题：基于神经网络的自适应动态编程以最大化生存概率PI名称：Abhijit GosaviPI研究所：密苏里科技大学大多数以前关于最优控制和自适应动态编程(ADP)的工作都集中在一种最佳情况下的优化，其中要控制的系统是稳定的，目标只是最大化收益或最小化成本。这位PI在对风险敏感的ADP这个小而重要的领域一直处于领先地位，ADP的目标仍然是实现利益最大化，但试图找到风险较低的策略来实现这一点。在这项新的工作中，PI提出了针对最坏情况的新方法，在这种情况下，稳定性或生存无法得到保证，而优化问题是最大化生存概率。这是一类非常重要的问题。此外，他计划通过开发新的软件工具，有效地将ADP应用于具有离散变量和连续变量的混合问题，从而弥合他迄今工作过的决策、管理和风险社区与工程ADP社区之间的差距。提议的智力优点：这将是将ADP(或至少是连续变量ADP)应用于极其重要的问题类别的第一项工作。例如，在试图理解大脑的作用时，最大化生存概率的挑战是非常重要的。风险敏感型ADP(RSADP)将首次应用于能够同时有效处理离散变量和连续变量的新兴ADP方法。建议的广泛影响：MatLab中针对这种优化方法的新工具可能对整个ADP领域的发展具有巨大的战略意义。这也可能对PI以及他在职业生涯现阶段试图开拓的领域带来比往常更大的好处。该项目的结果将为研究可使用ADP的工程中的许多问题奠定基础：车辆的路线选择、结构的维护(特别是在面临危险的自然灾害时)、航空公司的收入管理以及制成品的供应链。

Abstract Proposal Number: 0841055Proposal Title: Neural-Network-Based Adaptive Dynamic Programming for Maximizing Survival ProbabilitiesPI Name: Abhijit GosaviPI Institution: Missouri University of Science and TechnologyMost previous work on optimal control and adaptive dynamic programming (ADP) has focused on a kind of best-case optimization, where the system to be controlled is stable and the goal is simply to maximize benefits or minimize costs. This PI has been a leader in the small but important area of risk-sensitive ADP, which still aims to maximize benefits but tries to find less risky strategies for doing so. In this new work, the PI proposes to develop new methods aimed at the worst case situation, where stability or survival can not be guaranteed, and the optimization problem is to maximize the probability of survival. This is a very important class of problems. In addition, he plans to bridge the gap between the decision, management and risk community, where he has worked so far, and the engineering ADP community, by developing new software tools that can apply ADP efficiently to problems with a mix of discrete and continuous variables.Intellectual merit of proposal: This would be the first work to apply ADP (or at least continuous-variable ADP) to an extremely important class of problems. The challenging of maximizing a probability of survival is very important, for example, in trying to understand what brains do. Risk-sensitive ADP (RSADP) will be applied for the first time to the emerging class of ADP methods capable of efficiently coping with discrete and continuous variables at the same time.Broad impact of proposal: New tools in MatLab for this kind of optimization method could be of enormous strategic importance to the development of the entire ADP field. This could also be of more than usual benefit to the PI and to the area he has tried to pioneer at this stage of his career. The results of this project will build the foundation for studying RSADP on numerous problems in engineering where ADP can be used: routing of vehicles, maintenance of structures (especially in the face of risky natural disasters), revenue management in airlines, and supply chains of manufactured products.