Assessing Quality of Evolved Agent Controllers for Collective Gathering

The research goal was to engineer an agent collective that most effectively accomplished a cooperative gathering task. The proper setting of agent controller parameters was vital for the finding of agent behavior that most effectively gathered a high quantity and quality of resources in an unknown virtual environment. We tested the efficacy of evolutionary design of agent controller parameters, via testing evolved parameters in a non-evolutionary control experiment. The effectiveness of evolutionary design for the given task was further supported by comparing evolved parameters with a quality space sampling methodology that explored the parameter space for regions that produced a high value gathered cooperatively for the agent collective. Results indicated that the evolutionary approach was able to find agent controller settings for accomplishing the task with a high level of performance