| Abstract: |
We use simulated soccer to study multi-agent learning. Each
team member tries to learn from the corresponding human player in a real
game. Following a unified approach, strategic and tactical behavior is learned
synergistically by training a feed-forward neural network (ANN) with a modified
back-propagation algorithm. It aims at decreasing the learning time and
avoiding the local maximums. We tried to minimize the computation effort,
as required in classic back-propagation (BKP) methods. |