Integrating Dynamic Decision Making with Neurocontrollers by Combining System and Cognitive Sciences

项目概述本研究的目标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。这些问题是相当复杂的，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。方法是根据认知科学原理发展决策范式，但它们的数学表示将使用决策场理论。他们的解包含在神经网络中，将与另一组网络交互作用，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。智能优点本研究试图找到包含连续元素和离散元素的问题的统一控制器解。预计发展起来的数学认知科学思想将在计算神经科学和控制领域产生新的表征和问题解决结构。这项努力中提出的工作试图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。通过这项研究开发的更广泛的影响抽象和解决方案结构可用于后果或紧急管理系统，如管理地震的善后工作，恢复受损飞机的稳定性和可持续的运动和着陆，以及在应对威胁的突击反应中管理多项资产和分配。这项研究产生的决策结构也会对人机交互产生巨大影响。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。

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.