Learning Ant Foraging Behaviors

Abstract

Insects are good at cooperatively solving many complex tasks. For example, foraging for food far away from a nest can be solved through relatively simple behaviors in combination with pheromones. As task complexity increases, however, it may become difficult to find individual agent rules which yield a desired emergent cooperative behavior, or to know if any such rules exist at all. For such tasks, machine learning techniques like evolutionary computation (EC) may prove a valuable approach to searching the space of possible rule combinations. This paper presents an application of genetic programming to search for foraging behaviors. The learned foraging behaviors use only pheromone information to find the path to the nest and to the food source.