Automatic learning of evaluation functions for chess programs can be seen as the training of a machine learning model.
our learning algorithm is similar to the supervised approach (Tesauro, 2001;Hoki, 2006) in that the parameters are optimized based on the differences of the feature vectors realized by the correct and incorrect actions.