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.

该项目汇集了一位顶级控制工程师和一位认知神经科学家，以设计一个新的控制设计家族，该家族将复制和解释活大脑的关键功能，这些功能在工程学（或计算神经科学中使用的模型）中从未实现过。这项新的工作建立在弗兰克刘易斯以前的工作，在开发自适应控制器（RLADP），可以随着时间的推移，在面对非线性和挑战，需要远见，如电网的管理，最大限度地提高性能。在这里，该小组将试图解释和复制大脑如何处理挑战，这些挑战要求他们学习如何构建时间，多个时间范围的多层次决策，以及如何处理空间中的复杂结构，就像我们在管理复杂的基础设施网络时所需要的那样。这两个挑战基本上解决了当今最好的RLADP和哺乳动物大脑的最高能力之间的三个差距中的两个。 新的基础设计工作将投入到正在进行的电力微电网控制实验室工作中，这对于未来能够应对插电式混合动力汽车或屋顶太阳能等大规模渗透的电力分配网络的构建块是重要的。以前的工作在IEEE/Wiley的《RLADP手册》中进行了回顾，由刘易斯和Liu编辑。空间复杂性将通过考虑在图上定义的系统的最优控制来解决，例如电力和通信网络。博弈论的扩展，有关分布式或集体智慧的问题，也将被考虑。这项工作也将建立在共同PI Levine关于大脑机制模型的基础上，该模型涉及杏仁核中的情感门和前扣带皮层中的深思熟虑的决定。