The goal of dynamic difficulty balancing is to keep the user interested from the beginning to the end, providing a good level of challenge.
Ideally, the progression is automatic; players start at the beginner's level and the advanced features are brought in as the computer recognizes proficient play.Different approaches are found in the literature to address dynamic game difficulty balancing.
[1] Hunicke and Chapman's approach[2] 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.
Extending such an approach to include opponent modeling can be made through Spronck et al.′s dynamic scripting,[3][4] which assigns to each rule a probability of being picked.
Rule weights can be dynamically updated throughout the game, accordingly to the opponent skills, leading to adaptation to the specific user.
Andrade et al.[5] divide the DGB problem into two dimensions: competence (learn as well as possible) and performance (act just as well as necessary).
Then, online learning is used to continually adapt this initially built-in intelligence to each specific human opponent, in order to discover the most suitable strategy to play against him or her.
Similarly, if the game level becomes too easy, it will choose actions whose values are higher, possibly until it reaches the optimal performance.
[8] Based on this fundamental assumption, a metric for measuring the real time entertainment value of predator/prey games was introduced, and established as efficient and reliable by validation against human judgment.
Further studies by Yannakakis and Hallam[9] have shown that artificial neural networks (ANN) and fuzzy neural networks can extract a better estimator of player satisfaction than a human-designed one, given appropriate estimators of the challenge and curiosity (intrinsic qualitative factors for engaging gameplay according to Malone)[10] of the game and data on human players' preferences.
The model is usually constructed using machine learning techniques applied to game parameters derived from player-game interaction[11] and/or statistical features of player's physiological signals recorded during play.
This head-to-head shoot-em-up would aid whichever player had just been shot, by placing a fresh additional object, such as a Cactus plant, on their half of the play-field making it easier for them to hide.
The developer, Ritual Entertainment, claimed that players with widely different levels of ability could finish the game within a small range of time of each other.
[18] In 2005, Resident Evil 4 employed a system called the "Difficulty Scale", unknown to most players, as the only mention of it was in the Official Strategy Guide.
[19] God Hand, a 2006 video game developed by Clover Studio, directed by Resident Evil 4 director Shinji Mikami, and published by Capcom for the PlayStation 2, features a meter during gameplay that regulates enemy intelligence and strength.
Besides pacing, the Director also controls some video and audio elements of the game to set a mood for a boss encounter or to draw the players' attention to a certain area.
These items are distributed based on a driver's position in a way that is an example of dynamic game difficulty balancing.
In 2020, a class-action lawsuit in the United States District Court for the Northern District of California accused game developer Electronic Arts of using its patented Dynamic Difficulty Adjustment technology in three of its EA Sports franchises — Madden NFL, FIFA, and NHL — across all games ranging back to the 2017 versions.