Genetic Programming can be effectively used to create emergent behavior for a group of autonomous agents. In the process we call Offline Emergence Engineering, the behavior is at first bred in a Genetic Programming environment and then deployed to the agents in the real environment. In this article we
shortly describe our approach, introduce an extended behavioral rule syntax, and discuss the impact of the expressiveness of the behavioral description to the generation success, using two scenarios in comparison: the election problem and the
distributed critical section problem. We evaluate the results, formulating criteria
for the applicability of our approach.