Evolutionary robotics for quasi-ecosystem

The paper deals with behavioral evolution of multiple robots in a quasi-ecosystem. An ecosystem model composed of insects and plants, which are in a relationship of parasitism, is simulated in discrete cell space. In this ecosystem, the plants are easy to eliminate as the population size of the insects increases. Consequently, it is necessary to numerical balance plants and insects in the quasi-ecosystem. Therefore, multiple robots are introduced to remove some insects from the quasi-ecosystem. However, if the robots eliminate all the insects, viruses will eliminate the plants owing to diseases. In this ecosystem with complicated relationships, the robots should acquire strategies to maintain plants. We use simple if-then rules and apply genetics-based machine learning for acquiring a strategy for removing insects. Furthermore, we show several simulation results of behavior learning of multiple robots