2016/03/19 by Brulé, Joshua, Engel, Kevin, Fung, Nick +1
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE)
paper · doi:10.48550/arxiv.1603.06141
We apply genetic programming techniques to the `shepherding' problem, in which a group of one type of animal (sheep dogs) attempts to control the movements of a second group of animals (sheep) obeying flocking behavior. Our genetic programming algorithm evolves an expression tree that governs the movements of each dog. The operands of the tree are hand-selected features of the simulation environment that may allow the dogs to herd the sheep effectively. The algorithm uses tournament-style selection, crossover reproduction, and a point mutation. We find that the evolved solutions generalize well and outperform a (naive) human-designed algorithm.