In multiagent systems, the potential interactions between agents is combinatorial. Explicitly coding in each behavioral strategy is not an option. The agents can start with a default set of behavioral rules and adapt them on-line to fit in with their experiences. We investigate perhaps the simplest testbed for multiagent systems: the pursuit game. Four predator agents try to capture a prey agent. We show how different assumptions about the domain can drastically alter the need for learning. In one formulation there is no need for learning at all, simple greedy agents can effectively capture the prey (Korf 1992). As we remove layers abstraction, we find that learning is necessary for the predator agents to effectively capture the prey.