10/30/2022 0 Comments Algorithm for chess program![]() ![]() master in 1983, was more than 100 000 times slower. By contrast, the computer that executed Belle, the first program to earn the title of U.S. Downloaded on Apat 20:44 from IEEE Xplore. 12, DECEMBER 2004Īuthorized licensed use limited to: University of Pennsylvania. an official designation of grandmaster requires competition in sanctioned tournaments against other grandmasters. Deep Blue, the famous computer program and hardware (32 computing nodes, each with eight very-large-scale integrated processors) that defeated Kasparov in 1997, evaluated 200 million alternative #Algorithm for chess program softwareThis progress, however, has not arisen mainly because of any real improvements in anything that might be described as “artificial intelligence.” Instead, progress has come most directly from the increase in the speed of computer hardware, and also straightforward software optimization. During these five decades, the progress of chess programs in terms of their measured performance ratings has been steady. ![]() BACKGROUND For more than 50 years, the game of chess has served as a testing ground for efforts in artificial intelligence, both in terms of computers playing against other computers and computers playing against humans –. This protocol is adopted to indicate the versatility of the evolutionary approach. #Algorithm for chess program seriesNevertheless, the credit assignment approach has been adopted largely based on a long-standing belief that there is insufficient information in “win, lose, or draw” when referred to the entire game to “provide any feedback for learning at all over available time scales.”1 Experiments described here indicate that in contrast, a machine learning algorithm based on the principles of Darwinian evolution can adapt a chess program that plays at a rating that is commensurate with a grandmaster without relying on any specific credit assignment algorithms.2 Not only is the outcome of any specific game not used as direct feedback, only a point score reflecting performance over a series of games is used to judge the performance of competing programs. The final result is more than simply the sum of the attributed worth of each move in a series. Digital Object Identifier 10.1109/JPROC.2004.837633Įver, that describing any particular action as correct or incorrect may be vacuous because the end result is typically a nonlinear function of the interaction of the moves played by both players. The authors are with Natural Selection, Inc., La Jolla, CA 92037 USA (e-mail: ). This work was sponsored in part by the National Science Foundation under Grants DMI-0232124 and DMI-0349604. Manuscript received Jrevised September 7, 2004. The credit assignment problem then has been to find rules for rewarding “correct” actions and penalizing “incorrect” actions. In a game of strategy, such as chess, the final result of win, lose, or draw is associated with numerous decisions made from the first to the final move. Efforts in machine learning have often been focused on reinforcement learning, in which a series of actions leads to success or failure and some form of credit or blame for the final result is apportioned back to each action –. The challenge of creating machines that can learn from their own experience without significant human intervention has remained elusive. ![]() This approach, however, presents a significant limitation: artificial intelligence programs are restricted mainly to those problems that are, in large part, already solved. Simulating the symptoms of intelligent behavior rather than the mechanisms that underlie that behavior was a natural outgrowth of the pressure to design useful software applications. INTRODUCTION A fundamental approach to artificial intelligence has been to capture human expertise in the form of programmed rules of behavior that, when executed on a computer, emulate the behavior of the human expert. Keywords-Chess, computational intelligence, evolutionary computation, machine learning, neural networks. Testing under simulated tournament conditions against Pocket Fritz 2.0 indicated that the evolved program performs above the master level. During evolution, the program improved its play by almost 400 rating points. The program learned to evaluate chessboard configurations by using the positions of pieces, material and positional values, and neural networks to assess specific sections of the chessboard. Using an evolutionary algorithm, a computer program has learned to play chess by playing games against itself. HAHN, AND JAMES QUON Contributed Paper A central challenge of artificial intelligence is to create machines that can learn from their own experience and perform at the level of human experts. A Self-Learning Evolutionary Chess Program DAVID B. ![]()
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