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  3. Early in the film A Beautiful Mind, the mathematician John Nash is seen sitti

Early in the film A Beautiful Mind, the mathematician John Nash is seen sitti

游客2023-11-26  46
问题    Early in the film A Beautiful Mind, the mathematician John Nash is seen sitting in a Princeton courtyard, hunched over a playing board covered with small black and white pieces that look like pebbles. He was playing Go, an ancient Asian game. Frustration at losing that game inspired the real Nash to pursue the mathematics of game theory, research for which he eventually was awarded a Nobel Prize.
   In recent years, computer experts, particularly those specializing in artificial intelligence, have felt the same fascination and frustration. Programming other board games has been a relative snap. Even chess has succumbed to the power of the processor. Five years ago, a chess-playing computer called Deep Blue not only beat but thoroughly humbled Garry Kasparov, the world champion at that time. That is because chess, while highly complex, can be reduced to a matter of brute force computation. Go is different. Deceptively easy to learn, either for a computer or a human, it is a game of such depth and complexity that it can take years for a person to become a strong player. To date, no computer has been able to achieve a skill level beyond that of the casual player.
   The game is played on a board divided into a grid of 19 horizontal and 19 vertical lines. Black and white pieces called stones are placed one at a time on the grid’s intersections. The object is to acquire and defend territory by surrounding it with stones. Programmers working on Go see it as more accurate than chess in reflecting the ways the human mind works. The challenge of programming a computer to mimic that process goes to the core of artificial intelligence, which involves the study of learning and decision-making, strategic thinking, knowledge representation, pattern recognition and perhaps most intriguing, intuition.
   Along with intuition, pattern recognition is a large part of the game. While computers are good at crunching numbers, people are naturally good at matching patterns. Humans can recognize an acquaintance at a glance, even from the back.
   Daniel Bump, a mathematics professor at Stanford, works on a program called GNU Go in his spare time.
   "You can very quickly look at a chess game and see if there’s some major issue, " he said. But to make a decision in Go, he said, players must learn to combine their pattern-matching abilities with the logic and knowledge they have accrued in years of playing.
   Part of the challenge has to do with processing speed. The typical chess program can evaluate about 300, 000 positions in a second, and Deep Blue was able to evaluate some 200 million positions in a second. By midgame, most Go programs can evaluate only a couple of dozen positions each second, said Anders Kierulf, who wrote a program called SmartGo.
   In the course of a chess game, a player has an average of 25 to 35 moves available. In Go, on the other hand, a player can choose from an average of 240 moves. A Go-playing computer would need about 30, 000 years to look as far ahead as Deep Blue can with chess in three seconds, said Michael Reiss, a computer scientist in London. But the obstacles go deeper than processing power. Not only do Go programs have trouble evaluating positions quickly; they have trouble evaluating them correctly. Nonetheless, the allure of computer Go increases as the difficulties it poses encourage programmers to advance basic work in artificial intelligence.
   For that reason, Fotland said, "writing a strong Go program will teach us more about making computers think like people than writing a strong chess program." [br] According to the passage, what causes the fascination in programming a computer to play Go?
选项 A、The award of Nobel Prize.
B、The ambition of beating human in playing Go like Deep Blue did in playing chess.
C、It can advance basic work in artificial intelligence.
D、It can make computers behave like man.
答案 C
解析 根据文章，使科学家们沉迷于研究围棋程序编写的原因何在?文章在结尾处提到， “尽管围棋程序的开发如此之难，但编程人员愈发产生了浓厚的兴趣。”可见正因为其难，一旦攻克就可以在人工智能方面取得重大进展，这正是激励科学家们的最重要的原因，选项A、B流于表面，选项D错在“behave”，应该是“think”。
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