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

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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.