Information Hedging from Sensing to Cognition to ensure Coherent Decision Making

Maintaining consistent information flow between abstraction layers, from sensing to cognition, to ensure consistent decision-making is difficult. We are proposing an entirely new data structure, based on hypergraphs, to solve this problem. These hypergraph data structures will be developed in a domain specific language known as NodeLab and serve as a unified knowledge representation that can accept both low-level sensor data and high-level cognitive concepts, as well as maintain information coherence between the internal models of different abstraction layers. We will use these hypergraphs to conduct information hedging, whereby we reconcile discrepancies between the internal models of different software modules, and decide to either tolerate or eliminate these discrepancies. By doing so, we avoid contradictory actions that can result from modules having different internal models of the other module#39;s behavior.Our NodeLab hypergraphs will also be integrated with formal tools, such as Microsoft Z3, so that we can incorporate checks on information conversion and data constraints. Our NodeLab compiler will be able to use this information to generate certification artifacts that will allow us to streamline DO-178C processes and enable FAA certification of complex autonomy algorithms, a current barrier to the development of UAS technologies. We plan to use these technologies to develop certifiable autonomy software for urban air mobility (passenger-carrying aircraft in metropolitan areas). We will be leveraging our extensive experience in flying autonomous helicopters and our verifiable compiler development expertise, under which we are currently on contract with DARPA and Sikorsky Aircraft.nbsp;