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The Swift Gamma-Ray Burst Explorer is a uniquely capable mission, with threeon-board instruments and rapid slewing capabilities. It serves as afast-response satellite observatory for everything from gravitational-wavecounterpart searches to cometary science. Swift averages 125 differentobservations per day, and is consistently over-subscribed, responding to aboutone-hundred Target of Oportunity (ToO) requests per month from the generalastrophysics community, as well as co-pointing and follow-up agreements withmany other observatories. Since launch in 2004, the demands put on thespacecraft have grown consistently in terms of number and type of targets aswell as schedule complexity. To facilitate this growth, various schedulingtools and helper technologies have been built by the Swift team to continueimproving the scientific yield of the Swift mission. However, these tools havebeen used only to assist humans in exploring the local pareto surface and forfixing constraint violations. Because of the computational complexity of thescheduling task, no automation tool has been able to produce a plan of equal orhigher quality than that produced by a well-trained human, given the necessarytime constraints. In this proceeding we formalize the Swift Scheduling Problemas a dynamic fuzzy Constraint Satisfaction Problem (DF-CSP) and explore theglobal solution space. We detail here several approaches towards achieving thegoal of surpassing human quality schedules using classical optimization andalgorithmic techniques, as well as machine learning and recurrent neuralnetwork (RNN) methods. We then briefly discuss the increased scientific yieldand benefit to the wider astrophysics community that would result from thefurther development and adoption of these technologies.

Improving science yield for NASA Swift with automated planning technologies