Collaborative Research: Integrating dynamic decision making with neurocontrollers by combining system and cognitive sciences

合作研究：通过系统与认知科学的结合，将动态决策与神经控制器相结合

• 批准号: 1002188
• 负责人: Jerome Busemeyer
• 金额: $ 13.66万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1002188&HistoricalAwards=false
• 关键词: Collaborative; Research; Integrating; dynamic; decision

Project SummaryThe objective of this research is to develop new neural network structures to solve optimal control problems with dynamic decision making. These problems are quite complex since the system dynamics could switch modes at unknown times based on event based decision making. The approach is to develop the decision-making paradigms from cognitive science principles but their mathematical representations will use Decision Field Theory. Their solutions contained in neural networks will interact with another set of networks that embed solutions to the related optimal control problem formulated in an approximate dynamic programming framework.Intellectual Merit This research seeks to find unified controller solutions to problems which have both continuous and discrete elements in them. It is expected that the mathematical cognitive science ideas developed will lead to new representations and problem solving structures in computational neuroscience and control. The work proposed in this effort seeks to accomplish these objectives by offering a transformative approach that integrates concepts from system science and cognitive science. Broader Impact Abstractions and solution structures developed through this research can be used in consequence or emergency management systems like managing the aftermath of an earthquake, retrieving an impaired aircraft to stability and sustainable motion and landing, and managing multiple assets and allocation in striking responses to threats. Decision making structures resulting from this research can make tremendous impact on human-machine interactions too. For example, driver aid systems can be developed to augment human perception and enhance their cognition when they drive under impaired conditions.

本研究的目的是开发新的神经网络结构来解决具有动态决策的最优控制问题。这些问题非常复杂，因为系统动力学可以基于基于事件的决策在未知时间切换模式。该方法是从认知科学原理发展决策范式，但其数学表示将使用决策场理论。他们的解决方案包含在神经网络中，将与另一组网络相互作用，这些网络嵌入了在近似动态规划框架中制定的相关最优控制问题的解决方案。本研究旨在寻找具有连续和离散元素的问题的统一控制器解决方案。预计数学认知科学思想的发展将导致计算神经科学和控制领域的新表征和问题解决结构。在这项努力中提出的工作旨在通过提供一种整合系统科学和认知科学概念的变革性方法来实现这些目标。通过本研究开发的抽象和解决方案结构可用于后果或应急管理系统，如管理地震的善后，恢复受损飞机的稳定和可持续运动和着陆，以及管理多种资产和分配对威胁的惊人反应。由此产生的决策结构也会对人机交互产生巨大的影响。例如，可以开发驾驶员辅助系统，以增强人类在受损条件下驾驶时的感知能力和认知能力。