Evolving Shepherding Behavior with Genetic Programming Algorithms
Artificial Intelligence
2016-03-22 v1 Neural and Evolutionary Computing
Abstract
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.
Cite
@article{arxiv.1603.06141,
title = {Evolving Shepherding Behavior with Genetic Programming Algorithms},
author = {Joshua Brulé and Kevin Engel and Nick Fung and Isaac Julien},
journal= {arXiv preprint arXiv:1603.06141},
year = {2016}
}