Abstract:
A new competitive coevolutionary team-based particle
swarm optimiser (CCPSO(t)) algorithm is developed to
train multi-agent teams from zero knowledge. The CCPSO(t)
algorithm is applied to train a team of agents to play simple
soccer. The algorithm uses the charged particle swarm optimiser
in a competitive and cooperative coevolutionary training
environment to train neural network controllers for the
players. The CCPSO(t) algorithm makes use of the FIFA
league ranking relative fitness function to gather detailed
performance metrics from each game played. The training
performance and convergence behaviour of the particle swarm
is analysed. A hypothesis is presented that explains the lack
of convergence in the particle swarms. After applying a clustering
algorithm on the particle positions, a detailed visual
and quantitative analysis of the player strategies is presented.
The final results show that the CCPSO(t) algorithm is capable
of evolving complex gameplay strategies for a complex
non-deterministic game.
