Dynamic Difficulty Adjustment for Maximized Engagement in Digital Games

@article{Xue2017DynamicDA,
  title={Dynamic Difficulty Adjustment for Maximized Engagement in Digital Games},
  author={Su Xue and Meng Wu and John F. Kolen and Navid Aghdaie and Kazi A. Zaman},
  journal={Proceedings of the 26th International Conference on World Wide Web Companion},
  year={2017}
}
  • Su Xue, Meng Wu, Kazi A. Zaman
  • Published 3 April 2017
  • Computer Science
  • Proceedings of the 26th International Conference on World Wide Web Companion
Dynamic difficulty adjustment (DDA) is a technique for adaptively changing a game to make it easier or harder. A common paradigm to achieve DDA is through heuristic prediction and intervention, adjusting game difficulty once undesirable player states (e.g., boredom or frustration) are observed. Without quantitative objectives, it is impossible to optimize the strength of intervention and achieve the best effectiveness. In this paper, we propose a DDA framework with a global optimization… 
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41 Citations
Background Citations
18
Methods Citations
7
Results Citations
1

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41 Citations

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Citation Type

Dynamic Difficulty Adjustment (DDA) in Computer Games: A Review
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  • Computer Science
    Adv. Hum. Comput. Interact.
  • 2018
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This paper presents Fast Bayesian Content Adaption (FBCA), a system for DDA that is agnostic to the domain and that can target particular difficulties that significantly outperforms simpler DDA heuristics with the added benefit of maintaining a model of the user.
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TLDR
A method that automatically optimizes the user experience while taking into consideration other players and the macro constraints imposed by the game is proposed, based on a deep neural network architecture that involves a count loss constraint that has zero gradients in most of its support.
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TLDR
An idea for incorporating DDA through the use of Reinforcement Learning (RL) to agents of turn-based battle video games, and design and implement an RL agent that shows the idea of how a game could achieve balance through adequate choices in actions depending on the player's level of skill.
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This paper describes an own reinforcement learning algorithm that estimates difficulties of combat encounters during game runtime, which can be them used to find next suitable combat encounter of desired difficulty in a stochastic hill-climbing manner.
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TLDR
The approach can reliably find levels with a specific target difficulty for a variety of planning agents in only a few trials, while maintaining an understanding of their skill landscape.
Exploring Dynamic Difficulty Adjustment in Videogames
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This paper will present Dynamic Difficulty Adjustment (DDA), a recently arising research topic, which aims to develop an automated difficulty selection mechanism that keeps the player engaged and properly challenged, neither bored nor overwhelmed.
AlphaDDA: game artificial intelligence with dynamic difficulty adjustment using AlphaZero
TLDR
This study shows thatAlphaDDA can balance its skill with that of the other AI agents, except for a random player, and believes that the AlphaDDA approach can be used for any game in which the DNN can estimate the value from the state.
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