Paper: Player Modeling for Intelligent Difficulty Adjustment

Player Modeling for Intelligent Difficulty Adjustment
Olana Missura and Thomas Gärner [2009]

Automatic difficulty adjustment is a topic of great interest in game development. It can keep the player in the best possible 'fun' state and in the case of serious games, it can keep the player in the best state for whatever the game's objective is.

In this paper, the authors compare static to dinamic difficulty adjustment and explain their algorithm to determine a good difficulty setting for any player.

Traditionally, games where the player can adjust the difficulty involve the player choosing one of several difficulties and the game changing accordingly. The problem with this approach is that it's difficult for a developer to model the correct difficulty for each level of the game, as some might be perceived as too easy or too hard, depending on the player. To refine this, the developer needs to spend a great amount of resources to thoroughly test the difficulty settings of the game. The aim of this paper is to ease this process by modelling different players and trying to classify each player to set the difficulty accordingly.

The method they use for this purpose is quite interesting and it can be used in any game as long as the correct measurments are captured. They created a simple game with three difficulty settings. Game testers played the game setting the difficulty as they saw fit. While they were playing the game, data like score and player health was being recorded. After enough testers played the game, a clustering algorithm was applied to the data and was able to make a classification of the behavior of a player related to the difficulty they were playing in. So now, the only thing that has to be done is to classify a new player as fast as possible in the beginning of the game to automatically set the difficulty of the game.

This method had some technical limitations, like not every game during the same length of time and not every game ending the same way, but the data can be normalized to solve this problem.

They concluded that intelligently adjusting the difficulty was significantly better than the traditional static difficulty adjustment method and to further add to the investigation, different clasification algorithms should be tested, like nerual networks or gaussian processes.

Missura, Olana; Gärtner, Thomas. (2009). Player Modelling for Intellignet Difficulty Adjustment

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