Evolutionary design of autonomous agent based on gate growth

Presents an evolutionary method for robotics. Reactive architecture leads robots to move in the real world adaptively according to a quick response. However, designing adequate complete rules for complex behavior is practically impossible, because interaction among components is unlikely to be completely known. To approach this difficult design problem in the arbitration of the interaction organizing a system, a new approach “gate growth”, which is a grammar encoding network construction method, is presented. The methodology consists of two components: a cellular automaton (CA) as a connection algorithm and an NAND array as a device model. Through genetic algorithm operations, rules forming the CA are evolved to achieve given tasks. In application to a wall following problem with Khepera, the possibility of building task achieving behavior out of the gate growth is shown