Curiously, very little AI research has been involved in this expansion. Many video games do not use AI techniques, and those that do are usually based on relatively standard, labor-intensive scripting and authoring methods. In this and other respects, video games differ markedly from symbolic games. Video games often involve many agents embedded in a simulated physical environment where they interact through sensors and effectors that take on numerical rather than symbolic values. To be effective, agents must integrate noisy input from many sensors, react quickly, and change their behavior during the game. The AI techniques developed for and with symbolic games are not well suited to video games.

In contrast, machine-learning techniques, such as neural networks, evolutionary computing, and reinforcement learning, are very well suited to video games. Machine-learning techniques excel in exactly the kinds of fast, noisy, numerical, statistical, and changing domains that today’s video games provide. Therefore, just as symbolic games provided an opportunity for the development and testing of GOFAI techniques in the 1980s and 1990s, video games provide an opportunity for the development and testing of machine-learning techniques and their transfer to industry.


One of the main challenges for AI is creating intelligent agents that can become more proficient in their tasks over time and adapt to new situations as they occur. These abilities are crucial for robots deployed in human environments, as well as for various software agents that live in the Internet or serve as human assistants or collaborators.

Although current technology is still not sufficiently robust to deploy such systems in the real world, they are already feasible in video games. Modern video games provide complex artificial environments that can be controlled and carry less risk to human life than any real-world application (Laird and van Lent, 2000). At the same time, video gaming is an important human activity that occupies millions of people for countless hours. Machine learning can make video games more interesting and reduce their production costs (Fogel et al., 2004) and, in the long run, might also make it possible to train humans realistically in simulated, adaptive environments. Video gaming is, therefore, an important application of AI and an excellent platform for research in intelligent, adaptive agents.

Current video games include a variety of high-realism simulations of human-level control tasks, such as navigation, combat, and team and individual tactics and strategy. Some of these simulations involve traditional AI techniques, such as scripts, rules, and planning (Agre and Chapman, 1987; Maudlin et al., 1984), and a large part of AI development is devoted to path-finding algorithms, such as A*-search and simple behaviors built using finite-state machines. AI is

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