The use of evolutionary programming based on training examples for the generation of finite state machines for controlling objects with complex behavior

It is proposed to use evolutionary programming to generate finite state machines (FSMs) for controlling objects with complex behavior. The well-know approach in which the FSM performance is evaluated by simulation, which is typically time consuming, is replaced with comparison of the object’s behavior controlled by the FSM with the behavior of this object controlled by a human. A feature of the proposed approach is that it makes it possible to deal with objects that have not only discrete but also continuous parameters. The use of this approach is illustrated by designing an FSM controlling a model aircraft executing a loop-the-loop maneuver.