
Stanford computer scientists say that, with the diverse rules of every game in computers, they are the ideal tools for exploring ideas in artificial intelligence and fresh approaches to programming.
As Michael Genesereth, computer science professor with the Stanford Logic Group, and Nathaniel Love, a computer science doctoral student, in an article on general game playing (GGP), wrote that: "Programs that think better should be able to win more games.”
He added that, the theorem of general game playing is “drastically different” from the past creation of computer programming.
He said that, “the computer just follows a recipe that has been given to it.” AI’s application is limited to this situation because computer never needs to think for itself. According to him, program like IBM’s Deep Blue shows the smarts of the programmer rather than the smarts of the program. Writing program for GGP is similar as trying to educate a child the way how to play a game.
Genesereth said that competitions between GGP programs are an "evaluation technique for intelligent systems.” And, it is feasible to compare the relative intelligence of its system by playing the programs against one another.
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