Learning and adaptation in an airborne laser fire controller

A simulated battlefield, containing airborne lasers that shoot ballistic missiles down, provides an excellent test-bed for developing adaptive controllers. An airborne laser fire controller, which can adapt the strategy it uses for target selection, is developed. The approach is to transform a knowledge-based controller into an adaptable connectionist representation, use supervised training to initialize the weights so that the adaptable controller mimics the knowledge-based controller, and then use directed search with simulation-based performance evaluation to continuously adapt the controller behavior to the dynamic environmental conditions. New knowledge can be directly extracted from the automatically discovered controllers. Three directed search methods are characterized for production training, and compared with the better characterized gradient descent methods commonly used for supervised training. Automated discovery of improved controllers is demonstrated, as is automated adaptation of controller behavior to changes in environmental conditions.