Application of Genetic Programming for Generation of Controllers represented by Automata

Abstract This paper proposes an application of genetic programming for construction of state machines controlling systems with complex behavior. Application of this method is illustrated on example of unmanned aerial vehicle (UAV) control. It helps to find control strategies of collaborative behavior of UAV teams. Multi-agent approach is used, where every agent that controls a UAV is presented by a deterministic finite state machine. Two representations of finite state machines are used: abridged transition tables and decision trees. Novel algorithms for fixing connections between states and for removing unachievable branches of trees are proposed.