Machine Learning in Games. Machine Learning in Games Development. In this article, I shall outline the current perceptions of 'Machine Learning' in the games industry, some of the techniques and implementations used in current and future games, and then explain how to go about designing your very own 'Learning Agent'.
The Games Industry Machine Learning has been greeted with a certain amount of caution by games developers, and until recently, has not been used in any major games releases. Why is this -- surely there must be potential demand for games that can learn -- games that can adjust strategy to adapt to different opponents? There are several major reasons for the lack of enthusiasm which has, for a long time, been exhibited. Artificial intelligence (video games) In strategy games like Freeciv, the game AI must deal with large amounts of information Since game AI for NPCs is centered on appearance of intelligence and good gameplay within environment restrictions, its approach is very different from that of traditional AI; workarounds and cheats are acceptable and, in many cases, the computer abilities must be toned down to give human players a sense of fairness.
This, for example, is true in first-person shooter games, where NPCs' otherwise perfect aiming would be beyond human skill. Light cycle characters compete to be the last one riding, in GLtron. 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. Another development in recent game AI has been the development of "survival instinct". Genetic algorithm trying to build a car.