Experiences in evaluation with BKG—A program that plays backgammon

We here discuss insights gained about the structure of evaluation functions for a large domain such as backgammon. Evaluation began as a single linear polynomial of backgammon features. Later, we introduced Mate-classes, each with its own evaluation function. This improved the play, but caused problems with odge-effects between state-classes. Our latest effort uses models of position potential to select across the set of best members of each represented state-class. "This has produced a significant jump in performance of BKG. Because of the localization of knowledge, state-classes permit relatively easy modification of knowledge used in evaluation. They also permit the building of opponent models based upon what evidence shows the opponent knows in each state-class.