---
title: "Experiences in Evaluation with BKG - A Program that Plays Backgammon"
authors:
  - "Hans J. Berliner"
doi: ""
url: "https://ijcai.org/proceedings/1977/98"
pdf: "https://ijcai.org/proceedings/1977/0098.pdf"
venue: "Proceedings of the Fifth International Joint Conference on Artificial Intelligence"
year: 1977
pages: "428-432"
track: "Problem-solving and Search"
keywords:
---

# Experiences in Evaluation with BKG - A Program that Plays Backgammon

Hans J. Berliner

*Proceedings of the Fifth International Joint Conference on Artificial Intelligence*, pages 428-432.

## Abstract

Because of very high branching factors, a backgammon program must rely on knowledge rather than search for performance. We here disc.uss insights gained about the s tructure of evaluation functions for a large domain s uch as backgammon. Evaluation began as a single linear polynomial of backgammon features . Later, we introduced s tat e-c lasses, each with its own evaluation function .. This · improved the play, but cau se.d problems with e dge-effects between state -c lasses. Our latest effort use s models of position potential to select across the set of best members of each represented sta te-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 evaluati on ,. They also permit the building of opponent models ba se d upon what evidence shows the opponent knows in each state-class. Ou r program plays a generally competent game at an intermediate level of skill. It correctly solves a high percentage of intermediate level problems in books