With most computer games over the year people have only been given the illusion of intelligence, as we are not yet developed enough to create actual intelligence in our games. It is extremely difficult to plagiarize human prediction when it comes to games. In some cases humans can be extremely predictable e.g. In the game of American Football some teams always throw the ball where as other teams always run with the ball. In some cases however humans can be extremely unpredictable e.g.
             The way of unpredictable thinking is extremely difficult to simulate in a computer, as a computer cannot think for itself. When it comes to playing games with a computer the computer itself is not actually thinking it is programmed with all the predicted moves that a user can make. The programmer of the game actually sits down and programs all the moves that take place if a user does something. This then gives the user the illusion that the computer is coming up with way to try and beat them.
             This is where a new way of thinking came in. The idea was to actually simulate the way a human thinks by actually creating a computer simulated brain. This in theory anyway would allow the program to actually think and learn through trial and error.
             Neural networks grew out of research in Artificial Intelligence by attempting to mimic the fault-tolerance and capacity to learn of biological neural systems by modelling the low-level structure of the brain. The main branch of Artificial Intelligence research in the 1960s -1980s produced Expert Systems. These are based upon a high-level model of reasoning processes (specifically, the concept that our reasoning processes are built upon manipulation of symbols). It became rapidly apparent that these systems, although very useful in some domains, failed to capture certain key aspects of human intelligence. According to one line of speculation, this was due to their failure to mimic the underlying structure of ...