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`https://en.wikipedia.org/wiki/Dynamic_game_difficulty_balancing
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`Dynamic game difficulty balancing
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`Dynamic game difficulty balancing - Wikipedia, the free encyclopedia
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`From Wikipedia, the free encyclopedia
`
`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 them becoming bored (if the game is too easy) or frustrated (if it is too hard). The goal of dynamic
`difficulty balancing is to keep the user interested from the beginning to the end and to provide a good level of challenge for the user.
`
`Traditionally, game difficulty increases steadily along the course of the game (either in a smooth linear fashion, or through steps
`represented by the 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.
`
`Contents
`
`1 Dynamic game elements
`2 Approaches
`3 Uses in recent video games
`4 See also
`5 References
`6 Further reading
`7 External links
`
`Dynamic game elements
`
`Some elements of a game that might be changed via dynamic difficulty balancing include:
`
`Speed of enemies
`Health of enemies
`Frequency of enemies
`Frequency of powerups
`Power of player
`Power of enemies
`Duration of gameplay experience
`Approaches
`
`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:
`
`The rate of successful shots or hits
`The numbers of won and lost pieces
`Life points
`Evolution
`Time to complete some task
`
`... or any metric used to calculate a game score.
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`Hunicke and Chapman’s approach[1] controls the game environment settings in order to make challenges easier or harder. For
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`example, if the game is too hard, the player gets more weapons, recover life points faster or face fewer opponents. Although this
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`approach may be effective, its application can result in implausible situations. A straightforward approach is to combine such
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`"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.
`
`A traditional implementation of such an agent’s intelligence is to use behavior rules, defined during game development. A typical
`rule in a fighting game would state "punch opponent if he is reachable, chase him, otherwise". Extending such approach to include
`opponent modeling can be made through Spronck et al.′s dynamic scripting,[2][3] 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. With a simple mechanism, rules can be picked that generate tactics that are neither too strong nor too weak for the
`current player.
`
`Andrade et al.[4] divides the DGB problem into two dimensions: competence (learn as well as possible) and performance (act just as
`well as necessary). This dichotomy between competence and performance is well known and studied in linguistics, as proposed by
`Noam Chomsky. Their approach faces both dimensions with reinforcement learning (RL). Offline training is used to bootstrap the
`learning process. This can be done by letting the agent play against itself (selflearning), other pre-programmed agents, or human
`players. Then, online learning is used to adapt continually this initially built-in intelligence to each specific human opponent, in
`order to discover the most suitable strategy to play against him or her. Concerning performance their idea is to find an adequate
`policy for choosing actions that provide a good game balance, i.e., actions that keep both agent and human player at approximately
`the same performance level. According to the difficulty the player is facing, the agent chooses actions with high or low expected
`performance. For a given situation, if the game level is too hard, the agent does not choose the optimal action (provided by the RL
`framework), but chooses progressively less and less suboptimal actions until its performance is as good as the player’s. Similarly, if
`the game level becomes too easy, it will choose actions whose values are higher, possibly until it reaches the optimal performance.
`
`Demasi and Cruz[5] built intelligent agents employing genetic algorithms techniques to keep alive agents that best fit the user level.
`Online coevolution is used in order to speed up the learning process. Online coevolution uses pre-defined models (agents with good
`genetic features) as parents in the genetic operations, so that the evolution is biased by them. These models are constructed by
`offline training or by hand, when the agent genetic encoding is simple enough.
`
`Other work in the field of DGB is based on the hypothesis that the player-opponent interaction—rather than the audiovisual
`features, the context or the genre of the game—is the property that contributes the majority of the quality features of entertainment
`in a computer game.[6] 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[7] 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)[8] of the game and data on human players'
`preferences. The approach of constructing user models of the player of a game that can predict the answers to which variants of the
`game are more or less fun is defined as Entertainment Modeling. The model is usually constructed using machine learning
`techniques applied to game parameters derived from player-game interaction and/or statistical features of player's physiological
`signals recorded during play. This basic approach is applicable to a variety of games, both computer[7] and physical.
`Uses in recent video games
`
`The video game Flow was notable for popularizing the application of mental immersion (also called flow) to video games with its
`2006 Flash version. The video game design was based on the master's thesis of one of its authors, and was later adapted to
`PlayStation 3.
`
`The 2008 video game Left 4 Dead uses a new artificial intelligence technology dubbed "The AI Director".[9] The AI Director is
`used to procedurally generate a different experience for the players each time the game is played. It monitors individual players
`performance and how well they work together as a group to pace the game, determining the number of zombies that attack the
`player and the location of boss infected encounters based on information gathered. 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.[10]
`
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`Valve calls the way the Director is working "Procedural narrative" because instead of having a difficulty level which just ramps up
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`to a constant level, the A.I. analyzes how the players fared in the game so far, and try to add subsequent events that would give them
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`a sense of narrative.[11]
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`In 2009, the game Resident Evil 5 employed a system called the "Difficulty Scale", unknown to most players as the only mention of
`it was in the Official Strategy Guide. This system grades the players performance on a number scale from 1 to 10, and adjusts both
`enemy behavior/attacks used and enemy damage/resistance based on the players' performance (such as deaths, critical attacks, etc.).
`The selected difficulty levels lock players at a certain number, for example, on Normal difficulty one starts at Grade 4, can move
`down to Grade 2 if doing poorly, or up to Grade 7 if doing well. The grades between difficulties can overlap.[12]
`
`In the match-3 game Fishdom, the time limit is adjusted based on how well the player performs. The time limit is increased should
`the player fail a level, making it possible for any player to beat a level after a few tries.
`
`In the 1999 video game Homeworld, the number of ships that the AI begins with in each mission will be set depending on how
`powerful the game deems the player's fleet to be. Successful players have larger fleets because they take fewer losses. In this way, a
`player who is successful over a number of missions will begin to be challenged more and more as the game progresses.
`
`In the video games Fallout: New Vegas and Fallout 3, as the player increases in level, tougher variants of enemies, enemies with
`higher statistics and better weapons, or new enemies will replace older ones to retain a constant difficulty, which can be raised, using
`a slider, with experience bonuses and vice versa in Fallout 3. This can also be done in New Vegas, but there is no bonus to
`increasing or decreasing the difficulty.
`
`The Mario Kart series of games feature items during races that help the individual driver get ahead of opponents. These items are
`distributed based on a driver's position in a way that is an example of Dynamic Game Difficulty Balancing. For example, a driver
`near the bottom of the field is likely to get an item that will increase their speed, whereas a driver in first or second place can expect
`to get these kinds of items rarely.
`See also
`
`Difficulty level
`Nonlinear gameplay
`Game balance
`Game artificial intelligence
`Flow (psychology)
`References
`
`1. ^ Robin Hunicke, V. Chapman (2004). "AI for Dynamic
`Difficulty Adjustment in Games". Challenges in Game Artificial
`Intelligence AAAI Workshop. San Jose. pp. 91–96.
`2. ^ Pieter Spronck
`(https://web.archive.org/web/20111212020340/http://www.spronck.net/)
`from Tilburg centre for Creative Computing
`3. ^ P. Spronck, I. Sprinkhuizen-Kuyper, E. Postma (2004).
`"Difficulty Scaling of Game AI". Proceedings of the 5th
`International Conference on Intelligent Games and Simulation.
`Belgium. pp. 33–37.
`4. ^ G. Andrade, G. Ramalho, H. Santana, V. Corruble (2005).
`"Challenge-Sensitive Action Selection: an Application to Game
`Balancing". Proceedings of the IEEE/WIC/ACM International
`Conference on Intelligent Agent Technology (IAT-05).
`Compiègne, France: IEEE Computer Society. pp. 194–200.
`5. ^ P. Demasi, A. Cruz (2002). "Online Coevolution for Action
`Games". Proceedings of The 3rd International Conference on
`Intelligent Games And Simulation. London. pp. 113–120.
`6. ^ G. N. Yannakakis, J. Hallam (13-17 July 2004). "Evolving
`Opponents for Interesting Interactive Computer Games".
`Proceedings of the 8th International Conference on the
`Simulation of Adaptive Behavior (SAB'04); From Animals to
`
`Animats 8. Los Angeles, California, United States: The MIT
`Press. pp. 499–508.
`7. ^ a b G. N. Yannakakis, J. Hallam (18-20 May 2006). "Towards
`Capturing and Enhancing Entertainment in Computer Games".
`Proceedings of the 4th Hellenic Conference on Artificial
`Intelligence, Lecture Notes in Artificial Intelligence. Heraklion,
`Crete, Greece: Springer-Verlag. pp. 432–442.
`8. ^ Malone, T. W. (1981). "What makes computer games fun?".
`Byte 6: 258–277.
`9. ^ "Left 4 Dead"
`(https://web.archive.org/web/20111212020340/http://web.archive
`.org/web/20090316000000/http://l4d.com/info.html) . Valve
`Corporation. Archived from the original
`(https://web.archive.org/web/20111212020340/http://www.l4d.co
`m/info.html) on 2009-03-16.
`http://web.archive.org/web/20090316000000/http://l4d.com/info.
`html.
`10. ^ "Left 4 Dead Hands-on Preview"
`(https://web.archive.org/web/20111212020340/http://www.left4d
`ead411.com/left-4-dead-preview-pg2) . Left 4 Dead 411.
`http://www.left4dead411.com/left-4-dead-preview-pg2.
`11. ^ Newell, Gabe (21 November 2008). "Gabe Newell Writes for
`Edge"
`(https://web.archive.org/web/20111212020340/http://www.next-
`
`https://web.archive.org/web/20111212020340/https://en.wikipedia.org/wiki/Dynamic_game_difficulty_balancing
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`Dynamic game difficulty balancing - Wikipedia, the free encyclopedia
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`12. ^ Resident Evil 5 Official Strategy Guide. Prima Publishing. 5
`gen.biz/opinion/gabe-newell-writes-edge) . edge-online.com.
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`http://www.next-gen.biz/opinion/gabe-newell-writes-edge.
`March 2009.
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`Retrieved 2008-11-22.
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`Further reading
`
`Hunicke, Robin (2005). "The case for dynamic difficulty adjustment in games". Proceedings of the 2005 ACM SIGCHI
`International Conference on Advances in computer entertainment technology. New York: ACM. pp. 429–433.
`doi:10.1145/1178477.1178573
`(https://web.archive.org/web/20111212020340/http://dx.doi.org/10.1145%2F1178477.1178573) .
`Byrne, Edward (2004). Game Level Design. Charles River Media. p. 74. ISBN 1584503696.
`
`Chen, Jenova (2006). "Flow in Games"
`(https://web.archive.org/web/20111212020340/http://www.jenovachen.com/flowingames/abstract.htm) .
`http://www.jenovachen.com/flowingames/abstract.htm.
`External links
`
`Dynamic Difficulty Adjustment
`(https://web.archive.org/web/20111212020340/http://www.gameontology.org/index.php/Dynamic_Difficulty_Adjustment)
`- Game Ontology Wiki dead link
`
`Retrieved from "http://en.wikipedia.org/w/index.php?title=Dynamic_game_difficulty_balancing&oldid=445906345"
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`This page was last modified on 21 August 2011 at 00:15.
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`https://web.archive.org/web/20111212020340/https://en.wikipedia.org/wiki/Dynamic_game_difficulty_balancing
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