Inferring and Applying Safety Constraints to Guide an Ensemble of Planners for Airspace Deconfliction

This paper presents a Bayesian approach to learning flexible safety constraints in a coordinated, multi-planner ensemble, along with stochastic and active experimentation approaches for assigning degrees of blame when these constraints are violated. The blame is subsequently translated and conveyed to planners, for the purpose of improved overall system performance. To illustrate the advantages of our framework, we provide and discuss examples on a real test application for Airspace Control Order (ACO) planning and deconfliction, which is a benchmark application in the DARPA Integrated