10/13/2023 0 Comments Ajeeb chess automatonUse of alpha-beta pruning combined with a number of search heuristics dramatically improved the performance of brute-force search algorithms. In compensation, the program would be fast enough to look exhaustively at all positions to a certain depth within its allotted time. A program might, for example, pay attention only to checkmate, which side has more pieces, and which side has more possible moves, without any attempt at more complicated positional judgement. The second camp took a " brute force search" approach, examining as many positions as possible using the minimax algorithm with only the most basic evaluation function. Instead of wasting processing power examining bad or trivial moves (and their extensions), they tried to make their programs discriminate between bad, trivial and good moves, recognize patterns or formulate and execute plans, much as humans do. ![]() ![]() The first camp took a "strategic AI" approach, estimating that examining all possible sequences of moves to any reasonable depth would be impractical due to the astronomical number of possibilities and nominal processing power. In the early years of computer chess, there were two general schools of thought. Therefore, the fact that the best efforts of chess masters and computer engineers are as of 2004 so finely balanced should probably be viewed as an amusing quirk of fate rather than the profound comment on thought that many in the past, including some of the early theorists on machine intelligence, thought it to be. In chess, the combined skills of knowledgeable humans and computer chess engines can produce a result stronger than either alone (see Modern Chess Analysis by Robin Smith for a detailed discussion of how this works in practice.) In some strategy games, computers easily win every game, while in others they are regularly beaten even by amateurs. The brute-force methods are useless for most other problems artificial intelligence researchers have tackled, and are very different from how human chess players select their moves. Chess-playing programs essentially explore huge numbers of potential future moves by both players and apply a relatively simple evaluation function to the positions that result, whereas Computer Go challenges programmers to consider conceptual approaches to play. For this reason, computer chess, (as with other games, like Scrabble) is no longer of great academic interest to researchers in artificial intelligence, and has largely been replaced by more intuitive games such as Go as a testing paradigm. However, to the surprise and disappointment of many, chess has taught us little about building machines that offer human-like intelligence, or indeed do anything except play excellent chess. We can say that chess play is not an intractable problem to modern computing. The latter objective has largely been unrealized. For the first two purposes computer science has been a phenomenal success, from the earliest real attempts to programs which challenge the best human players in less than fifty years. ![]() The prime motivations for computerized chess playing have been solo entertainment (allowing players to practice and to amuse themselves when no human players are available), their use in chess analysis, and as research to provide insights into human cognition.
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