Assessing the performance of different behavior selection architectures in a large and complex virtual environment

We compare the performance of autonomous agents with three different behavior selection architectures (static-threshold, winner-takes-all and voting-based) in terms of survival in a large and complex dynamic virtual environment. Experiment results indicate both advantages and disadvantages when applying each architecture in such environmental conditions, and also shows that the performance of voting-based architecture is significantly sensitive to the balance of rates at which various environmental resources can serve to satisfy an agent´s physiological needs. Some problems with behavior selection architectures are identified and possible solutions are proposed