--%>

Computers playing games

How Computers playing games can be categorized according to different dimensions?

E

Expert

Verified

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).

   Related Questions in Basic Statistics

  • Q : Model Checking Approach Model Checking

    Model Checking Approach: • Specify program model and exhaustively evaluate that model against a speci?cation        –Check that properties hold   

  • Q : Safety and Liveness in Model Checking

    Safety and Liveness in Model Checking Approach; •? Safety: Nothing bad happens •? Liveness: Something good happens •? Model checking is especially good at verifying safety and liveness properties    –?Concurrency i

  • Q : Problems on ANOVA We are going to

    We are going to simulate an experiment where we are trying to see whether any of the four automated systems (labeled A, B, C, and D) that we use to produce our root beer result in a different specific gravity than any of the other systems. For this example, we would l

  • Q : Computing Average revenue using

    Can anyone help me in the illustrated problem? The airport branch of a car rental company maintains a fleet of 50 SUVs. The inter-arrival time between the requests for an SUV is 2.4 hrs, on an average, with a standard deviation of 2.4 hrs. There is no indication of a

  • Q : Compare the test results The grade

    The grade point averages of 61 students who completed a college course in financial accounting have a standard deviation of .790. The grade point averages of 17 students who dropped out of the same course have a standard deviation of .940. Do the data indicate a

  • Q : State the hypotheses At Western

    At Western University the historical mean of scholarship examination score for freshman applications is 900. Population standard deviation is assumed to be known as 180. Each year, the assistant dean uses a sample of applications to determine whether the mean ex

  • Q : Building Models Building Models • What

    Building Models • What do we need to know to build a model?– For model checking we need to specify behavior • Consider a simple vending machine – A custome rinserts coins, selects a beverage and receives a can of soda &bul

  • Q : Derived quantities in Queuing system

    Derived quantities in Queuing system: • λ = A / T, Arrival rate • X = C / T, Throughput or completion rate • ρ =U= B / T, Utilization &bu

  • Q : What is Interactive Response Time Law

    Interactive Response Time Law: • R = (L/X) - Z• Applies to closed systems.• Z is the think time. The time elapsed since&nb

  • Q : Define SPIN simulation modes SPIN: •

    SPIN: • SPIN generates C program that is the model checker – The pan verifier • Process Analyzer – Run the pan executable to do the model check