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.

该项目汇集了顶级控制工程师和认知神经科学家，旨在设计一系列新的控制设计，该设计将复制和解释活体大脑的关键功能，而这些功能在工程（或计算神经科学中使用的模型）中尚未实现。这项新工作建立在 Frank Lewis 之前的工作基础上，即开发自适应控制器 (RLADP)，该控制器可以在面对非线性和需要远见的挑战（例如电网管理）时，随着时间的推移最大限度地提高性能。在这里，该小组将尝试解释和复制大脑如何应对挑战，这些挑战要求他们学习如何构建时间，在多个时间范围内进行多层次的决策，以及如何处理空间中的复杂结构，就像我们管理复杂的基础设施网络所需要的那样。这两项挑战本质上解决了当今最好的 RLADP 与哺乳动物大脑最高能力之间的三个差距中的两个。 新的基础设计工作将融入正在进行的电力微电网控制实验室工作中，微电网作为未来配电网络的构建模块非常重要，能够应对插电式混合动力汽车或屋顶太阳能等的大规模普及。先前的工作在 IEEE/Wiley 的《RLADP 手册》中进行了回顾，由 Lewis 和 Liu 编辑。空间复杂性将通过考虑对图上定义的系统（例如电力和通信网络）的最优控制来解决。还将考虑与分布式或集体智慧问题相关的博弈论扩展。这项工作还将建立在联合首席研究员莱文关于大脑机制模型的研究基础上，该模型涉及杏仁核的情绪门和前扣带皮层的深思熟虑决策。