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Game playing was an area of research in AI from its inception. One of the first examples of AI is the computerized game of Nim made in 1951 and published in 1952. Despite being advanced technology in the year it was made, 20 years before Pong, the game took the form of a relatively small box and was able to regularly win games even against highly skilled players of the game. [1]
Chess is a turn-based strategy game that is considered a difficult AI problem due to the computational complexity of its board space. Similar strategy games are often solved with some form of a Minimax Tree Search. These types of AI agents have been known to beat professional human players, such as the historic 1997 Deep Blue versus Garry ...
General game playing (GGP) is the design of artificial intelligence programs to be able to play more than one game successfully. [ 1 ] [ 2 ] [ 3 ] For many games like chess, computers are programmed to play these games using a specially designed algorithm, which cannot be transferred to another context.
The AI would work like another human player which could impact the result of the game. “Sima needs only the images provided by the 3D environment and natural-language instructions given by the ...
Professional Go players see the game as requiring intuition, creative and strategic thinking. [1] [2] It has long been considered a difficult challenge in the field of artificial intelligence (AI) and is considerably more difficult to solve than chess. [3] Many in the field considered Go to require more elements that mimic human thought than ...
For example, SpeedTree is a middleware package that procedurally generates trees which can be used to quickly populate a forest. [1] Whereas most games use this technique to create a static environment for the final product, some employ procedural generation as a game mechanic , such as to create new environments for the player to explore.
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Generative artificial intelligence (generative AI, GenAI, [165] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 166 ] [ 167 ] [ 168 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 169 ...