New Adaptive Dynamic Programming Structures From Neurocognitive Psychology and Graphical Games

来自神经认知心理学和图形游戏的新自适应动态编程结构

• 批准号: 1405173
• 负责人: Frank Lewis
• 金额: $ 37.05万
• 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: 2014
• 资助国家: 美国
• 起止时间: 2014-07-15 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1405173&HistoricalAwards=false
• 关键词: New; Adaptive; Dynamic; Programming; Structures

This project brings together a top control engineer with a cognitive neuroscientist, in order to design a new family of control designs which would replicate and explain key capabilities of living brains which have never yet been achieved in engineering (or in the models used in computational neuroscience). This new work builds on previous work by Frank Lewis, in developing adaptive controllers (RLADP) which can maximize performance over time, in the face of nonlinearity and challenges which require foresight, such as the management of power grids. Here, the group will try to explain and replicate how brains can also handle challenges which require them to learn how to structure time, with multiple levels of decision with multiple time horizons, and how to handle complex structure in space, as we need to in managing complex infrastructure networks. These two challenges essentially address two of the three gaps between today's best RLADP and the highest capabilities of the mammal brain. The new fundamental design work will feed into ongoing laboratory work in the control of electric power microgrids, which are important as building blocks for future electric power distribution networks capable of coping with large penetrations of plug-in hybrid cars or rooftop solar and the like. The previous work is reviewed in the Handbook of RLADP, from IEEE/Wiley, edited by Lewis and Liu. Spatial complexity will be addressed by considering optimal control of systems defined over graphs, such as power and communication networks. Game theoretic extensions, related to issues of distributed or collective intelligence, will also be considered. The work will also build on work by co-PI Levine on models of mechanisms in the brain involving the emotional gates in the amygdala and deliberative decisions in the anterior cingulate cortex.

该项目将一名具有认知神经科学家的顶级控制工程师汇集在一起​​，以设计一个新的控制设计家族，该家族可以复制并解释活着的大脑的关键能力，这些功能在工程学中尚未实现（或计算神经科学中使用的模型）。这项新作品是基于弗兰克·刘易斯（Frank Lewis）的先前工作，在开发自适应控制器（RLADP）方面，它可以随着时间的推移最大程度地提高性能，面对需要远见的非线性和挑战，例如电网管理。在这里，小组将尝试解释和复制大脑如何处理挑战，这些挑战需要他们学习如何在多个时间范围内使用多个级别的决策，以及如何处理空间中的复杂结构，因为我们在管理复杂的基础架构网络时需要进行。这两个挑战实质上解决了当今最佳RLADP和哺乳动物大脑功能最高的三个差距中的两个差异。 新的基本设计工作将融入控制电动微电网的持续实验室工作，这是能够应对插件混合动力汽车或屋顶太阳能等大量渗透的未来电力发电网络的构建块。 Lewis和Liu编辑的IEEE/Wiley的RLADP手册中对先前的工作进行了审查。空间复杂性将通过考虑对图形上定义的系统（例如电源和通信网络）定义的系统来解决。还将考虑与分布式或集体智能问题有关的游戏理论扩展。这项工作还将基于Co-Pi Levine对大脑机制模型的工作，涉及杏仁核中的情绪大门和前扣带回皮层的审议决定。