Graham Kendall

Research Page



This page contains a history of computers playing checkers. This review is currently done from an academic point of view but will be expanded to cover the history of the game in due course.

You might also want to look at my home page (with navigation bar) or my game playing research page.

I am a lecturer at The University of Nottingham where I lecture on artificial intelligence and specialise on how computers can learn to play games without being told how to play (i.e. they are given no information as to game strategy, instead the computer has to "learn" the strategy for themselves). From an academic point of view I have published several articles on this research area (see my game playing articles).

The page contains references to the relevant academic literature (please let me know (by EMAIL) if I have missed any papers).

I have also listed some of the poker sites you might find of interest. If your site is not here, EMAIL me, and I'll include it.

This page is part of my game playing web site.


Arthur Samuel, in 1952 (Samuel, 1959), wrote the first checkers program. The original program was written for an IBM 701 computer. In 1954 he re-wrote the program for an IBM 704 and added a learning mechanism. What makes this program stand out in AI history is that the program was able to learn its own evaluation function. Taking into account the IBM 704 had only 10,000 words of main memory, magnetic tape for long-term storage and a cycle time of almost one-millisecond, this can be seen as a major achievement in the development of AI.

Samuel made the program play against itself and after only a few days play, the program was able to beat its creator and compete on equal terms with strong human opponents.

It remains as a testament to Samuel that there was little more work done on checkers until Jonathon Schaeffer et. al. developed Chinook (Schaeffer, all refs). This program uses alpha-beta search (which we consider below) and also has a database to allow it to play a perfect end game. In 1992 Chinook won the US Open and subsequently challenged for the world championship. Dr. Marion Tinsley had been the world champion for over 40 years. In that time he only lost three games. Playing Chinook he lost his fourth and fifth game but ultimately won the match by 21.5 points to Chinook’s 18.5 points. In August 1994 there was a re-match but the match ended prematurely when Dr. Tinsley had to withdraw for health reasons. As a result of this Chinook become the official world champion. Scheaffer claimed that Chinook was rated at 2814. The best human players are rated at 2632 and 2625. Chinook did not include any learning mechanisms.

More recently (Chellapilla, 1999), (Chellapilla, 2000) and (Fogel, 2001) developed a checkers program that "learnt" how to play a good game of checkers. The program started knowing just the rules of the game so that it could make legal moves. The program was allowed to evolve by creating a population of games that competed against one another, with the best games surviving and being adapted in some way before competing again. The adaptation was done using a neural network with the weights on the synapses being changed by an evolutionary strategy. The best program was allowed to compete against a commercial version of checkers and it beat it 6-0. The program got called Anaconda due to the way it put a strangle hold on its opponents.

I actually saw this program as the CEC conference at which the paper was presented. It challenged anybody who cared to play it and remain undefeated throughout the duration of the conference.




Last Updated 26th Mar 2001