Artificial intelligence in video games helps bring virtual worlds to life; it lurks beneath the surface, determining the way a player interacts with a game. As the brains of a game, AI engages our brains. There has been a great fascination in pitting the human expert against the computer.Game playing provided a high-visibility platform for this contest. It is important to note, however, that the performance of the human expert and the AI game-playing program reflect qualitatively different processes. More specifically, as mentioned earlier, the performance of the human expert utilizes a vast amount of domain specific knowledge and procedures. Such knowledge allows the human expert to generate a few promising moves for each game situation (irrelevant moves are never considered). In contrast, when selecting the best move, the game playing program exploits brute-force computational speed to explore as many alternative moves and consequences as possible.
AIS are often behind the characters you typically don't pay much attention to like race-car games like Need for Speed, strategy games like Civilization, or shooting games like Counter Strike.
Rather than learn how best to beat human players, AI in video games is designed to enhance human players gaming experience. The most common role for AI in video games is controlling non-player characters (NPCs). Designers often use tricks to make these NPCs look intelligent. One of the most widely used tricks, called the Finite State Machine algorithm. All NPC's behaviors are pre-programmed.
A more advanced method used to enhance the personalized gaming experience is the Monte Carlo Search Tree algorithm. MCST embodies the strategy of using random trials to solve a problem. This is the AI strategy used in Deep Blue, the first computer program to defeat a human chess champion in 1997. For each point in the game, Deep Blue would use the MCST to first consider all the possible moves it could make, then consider all the possible human player moves in response, then consider all its possible responding moves, and so on. You can imagine all of the possible moves expanding like the branches grow from a stem-that is why we call it "search tree". After repeating this process multiple times, the AI would calculate the payback and then decide the best branch to follow. After taking a real move, the AI would repeat the search tree again based on the outcomes that are still possible. In video games, an AI with MCST design can calculate thousands of possible moves and choose the ones with the best payback