Experiences in Evaluation with BKG - A Program That Plays Backgammon

Abstract

Because of very high branching factors, a backgammon program must rely on knowledge rather than search for performance. 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