Computers playing games
How Computers playing games can be categorized according to different dimensions?
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Computers playing games:Competing against each other in the form of a game is nothing new. Egyptians and Chinese have archived games which date back to far before the year zero. Games can be categorized according to different dimensions. Three examples are:
(1) the number of players,
(2) whether chance is involved, and
(3) how many information a player has.
With the upcoming of computers human beings were tempted to let the computer play those games. The reason why scientists are interested in research on board games is that the rules of games are mostly exact and well defined which makes it easy to translate them to a program that is suitable for a computer to run (Van den Herik, 1983). The research in board games obtained a huge impulse in 1944 when Von Neumann republished his article about the minimax algorithm (Von Neumann, 1928) together with Morgenstern in the book “Theory of Games and Economic Behavior” (Von Neumann and Morgenstern, 1944). These ideas were picked up by Shannon (1950) and Turing (1953) who tried to let a computer play Chess as intelligently as possible. Since then much research is performed on new methods, on a variety of games (Murray, 1952) and on other problems to make the computer a worthy opponent for the human player (Schaeffer and Van den Herik, 2002). One field in this area of research are the board games which have full information and are played by two persons. Chess is the classical example of this kind of a game and a great deal of effort has been devoted in the past to the construction of a good chess player. The most pregnant success so far in this area was the result when Deep Blue achieved to win against world chess champion Garry Kasparov (Newborn, 1996).
Operational Analysis: • Analysis method based on the measurement of the operational characteristics of the system. Q : Average think time Software monitor Software monitor data for an interactive system shows a CPU utilization of 75%, a 3 second CPU service demand, a response time of 15 seconds, and 10 active users. Determine the average think time of these users?
Software monitor data for an interactive system shows a CPU utilization of 75%, a 3 second CPU service demand, a response time of 15 seconds, and 10 active users. Determine the average think time of these users?
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