Machine Learning
Everything old is new again: a fresh look at historical approaches in machine learning
Everything old is new again: a fresh look at historical approaches in machine learning
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Player modeling impact on player's entertainment in computer games
UM'05 Proceedings of the 10th international conference on User Modeling
Towards player-driven procedural content generation
Proceedings of the 9th conference on Computing Frontiers
Dynamic difficulty balancing for cautious players and risk takers
International Journal of Computer Games Technology
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In this paper we aim at automatically adjusting the difficulty of computer games by clustering players into different types and supervised prediction of the type from short traces of gameplay. An important ingredient of video games is to challenge players by providing them with tasks of appropriate and increasing difficulty. How this difficulty should be chosen and increase over time strongly depends on the ability, experience, perception and learning curve of each individual player. It is a subjective parameter that is very difficult to set. Wrong choices can easily lead to players stopping to play the game as they get bored (if underburdened) or frustrated (if overburdened). An ideal game should be able to adjust its difficulty dynamically governed by the player's performance. Modern video games utilise a game-testing process to investigate among other factors the perceived difficulty for a multitude of players. In this paper, we investigate how machine learning techniques can be used for automatic difficulty adjustment. Our experiments confirm the potential of machine learning in this application.