Artificial intelligence (AI) in video games refers to the computational systems that control non-player characters (NPCs), generate dynamic game behavior, or simulate strategic decision-making. In practice, the term covers a broad range of techniques drawn from computer science, control theory, and robotics rather than "true" artificial intelligence. Moreover, academic AI—aimed at developing autonomous reasoning or learning—is distinct from game AI, whose goal is to deliver engaging, believable, and efficient behavior for players. The term CPU (central processing unit) in video games refers to the AI that control an NPC that could be a playable character in some other situations.
In general, game AI does not, as might be thought and sometimes is depicted to be the case, mean a realization of an artificial person corresponding to an NPC in the manner of the Turing test or an artificial general intelligence (AGI).
Overview The term game AI is used to refer to a broad set of algorithms that also include techniques from control theory, robotics, computer graphics, and computer science in general. Therefore, video game AI may often not constitute "true AI" in that such techniques do not necessarily facilitate computer learning or other standard criteria, only constituting "automated computation" or a predetermined and limited set of responses to a predetermined and limited set of inputs.
Some commentators in the video game industry have claimed that advances in game AI have transformed how players interact with digital systems more broadly. However, such claims remain disputed. Marketing discussions sometimes attribute human-like traits to game AI—such as personality or creative autonomy—but these are often scripted behaviors designed to mimic complexity rather than emergent cognition.
History Game playing has been an area of research in AI from its inception. One of the first examples of AI is the computerized strategy game Nim, made in 1940 and published in 1942. 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 against highly skilled players. In 1951, using the Ferranti Mark 1 machine of the University of Manchester, Christopher Strachey wrote a checkers program, and Dietrich Prinz wrote one for chess.See "A Brief History of Computing" at AlanTuring.net. These were among the first computer programs ever written. Arthur Samuel's checkers program, developed in the mid-1950s and early-1960s, eventually achieved sufficient skill to challenge a respectable amateur., Schaeffer, Jonathan. One Jump Ahead:: Challenging Human Supremacy in Checkers, 1997, 2009, Springer, . Chapter 6. Work on checkers and chess would culminate in the defeat of Garry Kasparov by IBM's Deep Blue computer in 1997., pp. 480–483 The first video games developed in the 1960s and early 1970s, like Spacewar! (1962), Pong (1972), and Gotcha (1973), were games implemented on discrete logic and strictly based on the competition of two players, without AI.
Games that featured a single player mode with enemies started appearing in the 1970s. The first notable ones for the arcade were the Taito game Speed Race (racing video game) in 1974 and the Atari games Qwak (duck hunting light gun shooter) in 1974 and Pursuit (fighter aircraft dogfighting simulator) in 1975. Two text-based computer games, Star Trek (1971) and Hunt the Wumpus (1973), also had enemies. Enemy movement was based on stored patterns. The incorporation of microprocessors would allow more computation and random elements overlaid into movement patterns.
It was during the golden age of video arcade games that the idea of AI opponents was largely popularized, due to the success of Space Invaders (1978), which sported an increasing difficulty level, distinct movement patterns, and in-game events dependent on hash functions based on the player's input. Galaxian (1979) added more complex and varied enemy movements, including maneuvers by individual enemies who break out of formation. Pac-Man (1980) introduced AI patterns to maze games, with the added quirk of different personalities for each enemy. Karate Champ (1984) later introduced AI patterns to fighting games. First Queen (1988) was a tactical action RPG (role-playing game) which featured characters that can be controlled by the computer's AI in following the leader. (Translation) The RPG Dragon Quest IV (1990) introduced a "Tactics" system, where the user can adjust the AI routines of non-player characters during battle, a concept later introduced to the action RPG genre by Secret of Mana (1993).
Games like Madden Football (1988), Earl Weaver Baseball (1987), and Tony La Russa Baseball (1991) used AI in an attempt to duplicate the coaching or managerial style of the selected celebrity. Madden, Weaver, and La Russa all worked with the game development teams to maximize the accuracy of the games. Later sports titles allowed users to "tune" variables in the AI to produce a player-defined managerial or coaching strategy.
The emergence of new game genres in the 1990s prompted the use of formal AI tools like finite-state machines. Real-time strategy games taxed the AI with many objects, incomplete information, pathfinding problems, real-time decisions, and economic planning, among other things.Schwab, 2004, pp. 97–112 The first games of the genre had notorious problems. Herzog Zwei (1989), for example, had almost broken pathfinding and very basic three-state state machines for unit control, and Dune II (1992) attacked the players' base in a beeline and used numerous cheats.Schwab, 2004, p. 107 Later games in the genre exhibited more sophisticated AI.
Later games have used bottom-up AI methods, such as the emergent behavior and evaluation of player actions in games like Creatures or Black & White. Façade was released in 2005 and used interactive multiple way dialogs and AI as the main aspect of game.
Games have provided an environment for developing AI with potential applications beyond gameplay. Examples include Watson, a Jeopardy!-playing computer; and the RoboCup tournament, where robots are trained to compete in soccer.
Views Many experts complain that the "AI" in the term game AI overstates its worth, as game AI is not about intelligence, and shares few of the objectives of the academic field of AI. Whereas "real AI" addresses fields of machine learning, decision making based on arbitrary data input, and even the ultimate goal of strong AI that can reason, "game AI" often consists of a half-dozen rules of thumb, or heuristics, that are just enough to give a good gameplay experience. These rules are designed to create the illusion of intelligence and work off of three core principles: the player desiring to believe that there is human-level intelligence in the games they play, the human urge to anthropomorphize nonhuman entities, and finally the power of expectation enhancing the player experience. These principles are further enhanced in the quality and performance of the game AI, "selling the illusion" through animations that are fluid and not robotic, dialog that fits the situation, giving the AI a reason to exist and a personality beyond the player, and having the appropriate reaction to the situation and environment.Rabin, S. (2017). Game AI Pro 3: Collected Wisdom of Game AI Professionals (Vol. 3). CRC Press LLC. https://www.gameaipro.com/GameAIPro3/GameAIPro3_Chapter01_The_Illusion_of_Intelligence.pdf Historically, academic game-AI projects have been relatively separate from commercial products because the academic approaches tended to be simple and non-scalable. Commercial game AI has developed its own set of tools, which have been sufficient to give good performance in many cases.
Developers Game developers' increasing awareness of academic AI and a growing interest in computer games by the academic community is causing the definition of what counts as AI in a game to become less idiosyncratic. Nevertheless, significant differences between varied application domains of AI mean that game AI can still be viewed as a distinct subfield of AI. In particular, the ability to legitimately solve some AI problems in games by cheating creates an important distinction. For example, inferring the position of an unseen object from past observations can be a difficult problem when AI is applied to robotics, but in a computer game, an NPC can simply look up the position in the game's scene graph. Such cheating can lead to unrealistic behavior and so is not always desirable. But its possibility serves to distinguish game AI and leads to new problems to solve, such as when and how to cheat.
The major limitation to strong AI is the inherent depth of thinking and the extreme complexity of the decision-making process. This means that although it would be theoretically possible to make "smart" AI, the problem would take considerable processing power.
Among developers, AI has been both widely adopted and controversial. In a 2025 report from the Game Developers Conference, 52% of 3,000 developers surveyed reported working at companies using GenAI. However, 30% of developers said they felt negatively about AI, compared to 18% the previous year.
With the rise of GenAI use, there have also been instances of game developers being falsely accused of using GenAI and AI-generated images, such as Magic: The Gathering and Nintendo's Mario Kart World, respectively. Both have denied the claims.
Gamers Video game players (gamers) have had mixed views toward AI use in video games, with particular skepticism given to how and where AI is used. The most frequently cited issue is "fear of lower quality and a loss of uniqueness", with gamers being more accepting of AI "helping behind the scenes or making NPCs smarter" and less accepting of AI replacing artists in the areas of story writing, soundtrack composition, character voice acting, and the like.
A report by MIDiA, published in December 2024, stated that "the majority of gamers – even daily players – are neutral about developers using GenAI".
A 2025 survey of gamers by Quantic Foundry found that "85% of respondents [had] a below-neutral attitude towards the use of GenAI in video games, with a highly-skewed 63% who selected the most negative response option". The survey also found attitudes varied by gender and age, with female and non-binary gamers being more negative than male gamers, and younger gamers being more negative than older ones.
In Bain & Company's 2026 gaming report, gamers' level of comfort with the game industry's use of AI in video games was questioned, with 14% saying they had become "significantly more comfortable" and 28% saying they had become "somewhat more comfortable".
Usage In computer simulations of board games Computer chess Computer shogi Computer Go Computer checkers Computer Othello Computer poker players Akinator Computer Arimaa Logistello, which plays Reversi Rog-O-Matic, which plays Rogue Computer players of Scrabble A variety of board games in the Computer Olympiad General game playing Solved games have a computer strategy which is guaranteed to be optimal, and in some cases force a win or draw.
In modern video games
Game AI/heuristic algorithms are used in a wide variety of disparate fields inside a game. The most obvious is in the control of NPCs in a game, although "scripting" (decision tree) is a common means of control. These handwritten decision trees often result in "artificial stupidity" such as repetitive behavior, loss of immersion, or abnormal behavior in situations the developers did not plan for.Lara-Cabrera, R., Nogueira-Collazo, M., Cotta, C., & Fernández-Leiva, A. J. (2015). Game artificial intelligence: challenges for the scientific community.