Local vs Global Models in Pong
We review two previous simulations in which opponent modelling was performed within the computer game of pong. These results suggested that sums of local models were better than a single global model on this data set. We compare two supervised methods, the multilayered perceptron, which is global, and the radial basis function network which is a sum of local models on this data and again find that the latter gives better performance. Finally we introduce a new topology preserving network which can give very local or more global estimates of results and show that, while the local estimates are more accurate, they result in game play which is less human-like in behaviour.