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A Gamut of Games

AI Magazine

In 1950, Claude Shannon published his seminal work on how to program a computer to play chess. Since then, developing game-playing programs that can compete with (and even exceed) the abilities of the human world champions has been a long-sought-after goal of the AI research community. In Shannon's time, it would have seemed unlikely that only a scant 50 years would be needed to develop programs that play world-class backgammon, checkers, chess, Othello, and Scrabble. These remarkable achievements are the result of a better understanding of the problems being solved, major algorithmic insights, and tremendous advances in hardware technology. Computer games research is one of the important success stories of AI. This article reviews the past successes, current projects, and future research directions for AI using computer games as a research test bed.


Game Playing: The Next Moves

AAAI Conferences

Computer programs now play many board games as well or better than the most expert humans. Human players, however, learn, plan, allocate resources, and integrate multiple streams of knowledge. This paper highlights recent achievements in game playing, describes some cognitively-oriented work, and poses three related challenge problems for the AI community.


A Re-Examination of Brute-Force Search

AAAI Conferences

In August 1992, the World Checkers Champion, Dr. Marion Tinsley, defended his title against the computer program Chinook. The best-of-40-game match was won by Tinsley with 4 wins to the program's 2. This was the first time in history that a program played for a human World Championship. Chinook, with its deep search and endgame databases, has established itself as a Grandmaster checker player. However, the match demonstrated that current brute-force game-playing techniques alone will be insufficient to defeat human champions in games as complex as checkers. This paper reexamines brute-force search and uses anecdotal evidence to argue that there comes a point where additional search is not cost effective. This limit, which we believe we are close to in checkers, becomes an obstacle to further progress. The problems of deep brute-force search described in this paper must be addressed before computers will be dominant in games such as checkers and chess.


Using Selective-Sampling Simulations in Poker

AAAI Conferences

The research efforts in computer game-playing have concentrated on building high-performance chess programs. With the Deep Blue victory over World Chess Champion Garry Kasparov, a milestone has been achieved but, more importantly, the artificial intelligence community has been liberated from the chess "problem". The consequence is that in recent years a number of interesting games have attracted the attention of AI researchers; games whose research results promise a wider range of applicability than has been seen for chess. Computer success has been achieved in deterministic perfect information games like chess, checkers and Othello, largely due to so-called brute-force search. The correlation of search speed to program performance gave an easy recipe to program success: build a faster search engine.