Dynamic game difficulty balancing

Dynamic game difficulty balancing, also known as dynamic difficulty adjustment (DDA) or dynamic game balancing (DGB), is the process of automatically changing parameters, scenarios, and behaviors in a video game in real-time, based on the player's ability, in order to avoid making the player bored (if the game is too easy) or frustrated (if it is too hard). However, letting AI players break the rules to which players are bound can cause the AI to cheat—for example, AI players might be given unlimited speed in racing games to stay near the human player. The goal of dynamic difficulty balancing is to keep the user interested from the beginning to the end, providing a good level of challenge.

Traditionally, game difficulty increases steadily along the course of the game (either in a smooth linear fashion, or through steps represented by levels). The parameters of this increase (rate, frequency, starting levels) can only be modulated at the beginning of the experience by selecting a difficulty level. Still, this can lead to a frustrating experience for both experienced and inexperienced gamers, as they attempt to follow a preselected learning or difficulty curve. Dynamic difficulty balancing attempts to remedy this issue by creating a tailor-made experience for each gamer. As the users' skills improve through time (as they make progress via learning), the level of the challenges should also continually increase. However, implementing such elements poses many challenges to game developers; as a result, this method of gameplay is not widespread.

Some elements of a game that might be changed via dynamic difficulty balancing include:

Different approaches are found in the literature to address dynamic game difficulty balancing. In all cases, it is necessary to measure, implicitly or explicitly, the difficulty the user is facing at a given moment. This measure can be performed by a heuristic function, which some authors call "challenge function". This function maps a given game state into a value that specifies how easy or difficult the game feels to the user at a specific moment. Examples of heuristics used are:

... or any metric used to calculate a game score. Hunicke and Chapman’s approach controls the game environment settings in order to make challenges easier or harder. For example, if the game is too hard, the player gets more weapons, recovers life points faster, or faces fewer opponents. Although this approach may be effective, its application can result in implausible situations. A straightforward approach is to combine such "parameters manipulation" to some mechanisms to modify the behavior of the non-player characters (NPCs) (characters controlled by the computer and usually modeled as intelligent agents). This adjustment, however, should be made with moderation, to avoid the 'rubber band' effect. One example of this effect in a racing game would involve the AI driver's vehicles becoming significantly faster when behind the player's vehicle, and significantly slower while in front, as if the two vehicles were connected by a large rubber band.

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