The most famous chess-playing computer was IBM’s Deep Blue. In 1997, Deep Blue defeated Garry Kasparov, the world’s top-ranked chess player, marking the first time a human champion was defeated by a computer (Campbell et al., 2002). Scientists are still studying the reasons for Deep Blue’s success in a study published in a recent issue of Artificial Intelligence. IBM’s dismantling of the computer following the match largely caused the lengthy delay in thoroughly analyzing the match by obfuscating efforts to study it (Kasparov, 2003)
One of the major factors leading to Deep Blue’s victory was its system of “game tree searches” (Campbell et al., 2002). A game tree search is a method of analysis that allows a “thinker” to narrow its focus on a few key issues by having a central node distribute commands to secondary chips (Campbell et al., 2002). A chess game begins with 32 pieces, many of which can make a wide variety of moves, moving in multiple directions and at varying distances. Imagine trying to decide on a move, analyzing what would happen for each move by an opponent, and your response to this. It is easy to see that the number of potential moves is enormous. This is because it is the result of an exponential function.
For example, if each player had 8 pieces that could move an average of 5 different ways, then after 5 moves by each player, the number of potential combinations of moves numbers in the quadrillions (4010), too many for even the fastest computer to evaluate within time limits. Yet most chess players search deeper than five moves into matches. To alleviate this problem, Deep Blue “decided” early in the search which moves were unlikely to lead to any productive position (Campbell et al., 2002), in other words, moves that would not result in jeopardizing the opposition’s pieces or would cause danger to the computer’s own pieces. This saved time, a very important advantage in this match because the first forty moves of each player were required to be played in two hours (Campbell et al., 2002). This also would be critical in other machines employing similar methods, such as a video game, which also are limited by time constraints.
The programming further augmented Deep Blue’s strength. The program gave “credit” for the advantages of certain positions. For example, a move that is significantly better than every alternative, i.e., one that saved one’s own pieces while endangering those of the opposition, received a large credit, marking it as a superior move and more likely to be played (Campbell et al., 2002). Another vital element was the determination that if a move is good, it is better played sooner rather than later (Campbell et al., 2002). This allowed the program to search deeper into the match because it wasted less time contemplating moves already evaluated and deemed insufficient by the computer.