Learning by Parameter Adjustment

Many programs rely on an evaluation procedure to summarise the state of search etc. Game playing programs provide many examples of this.

However, many programs have a static evaluation function.

In learning a slight modification of the formulation of the evaluation of the problem is required.

Here the problem has an evaluation function that is represented as a polynomial of the form such as:

The t terms a values of features and the c terms are weights.

In designing programs it is often difficult to decide on the exact value to give each weight initially.

So the basic idea of idea of parameter adjustment is to:

• Start with some estimate of the correct weight settings.
• Modify the weight in the program on the basis of accumulated experiences.
• Features that appear to be good predictors will have their weights increased and bad ones will be decreased.

Samuel's Checkers programs employed 16 such features at any one time chosen from a pool of 38.