Dynamic Air Battle Planning

Tuesday, August 01 2006

Software systems employing genetic processing algorithms rapidly create valid air tasking orders.

An air tasking order (ATO) implements air operations supporting the joint force commander’s campaign by assigning aircraft and munitions to targets and specifying the timing and grouping of air missions. Currently, creating an ATO is a routine, but manually intensive process that underutilizes the skills of the Master Air Attack Plan (MAAP) chief and his or her team. The current ATO creation software employs constraint-based linear programming and indicates only if a proposed mission is valid; it does not identify whether the ATO is the optimal plan given the MAAP team’s objectives, target list, and available inventories of aircraft and munitions. Software engineers from 21st Century Technologies (Austin, Texas) are developing two products— ATO-Link and ATO-Stream—to automate and optimize the ATO creation process, shorten the ATO development cycle to minutes, and reduce mundane planning work. AFRL’s Small Business Innovation Research program is administering both project efforts.

ATO-Link and ATO-Stream are genetic-algorithm-based software systems that automatically generate a valid ATO tailored to battlefield conditions and campaign objectives. ATO-Link implements a fixed air campaign valid for a 24- hour period, allocating limited inventories of aircraft and munitions to achieve multiple objectives, while honoring numerous complex constraints. ATO-Stream leverages ATO-Link capabilities to manipulate a kinetic stream of targets, resources, and battlefield conditions and produce a continuously updated ATO. The ATO-Stream product dynamically considers a host of competing and changing priorities and evaluates changes in the status of air resources to perform continuous ATO planning and replanning.

The 21st Century Technologies team chose a genetic processing approach to solve the multiple-goal, multipleconstraint problems posed by ATO-Link and ATO-Stream. In this approach, each solution generates numerous ATOs with the routine, assigning each ATO a “fitness” score. The fitness score is a function of the quantity and attributes of all serviced targets, including aircraft characteristics, risk of aircraft loss, target size, and probability of target destruction. ATO solutions with the highest fitness scores—designated “elite”—live (unchanged) into the next generation, whereas solutions with the lowest scores immediately die out. The algorithm retains ATOs earning midrange scores, combining (breeding) them with elite solutions in the hope of forming everhigher- scoring solutions in the next generation—solutions which would then replace the elite of the current generation. The algorithm thus mimics the biological “survive and reproduce” process. The accompanying figure depicts an overview of the genetic processing approach as applied to ATO planning.