Neuro-Fuzzy Dynamic Difficulty Adjustment for Computer Games



  • Theekshana Dissanayake
  • Yasitha Rajapaksha
  • Heshan Sandeepa


  • Prof. Roshan G. Ragel
  • Dr. Isuru Nawinne


Dynamic Difficulty Adjustment (DDA) in computer games is a relatively new research area which focuses on improving the gaming experience by adjusting the difficulty level of the game depending on user performance. This article focuses on the design and the implementation of a performance-based DDA system which employs a combination of a neural network and a fuzzy system. In this system, a multilayer perceptron (MLP) neural network act as the difficulty detector and a fuzzy system act as the difficulty adjuster. Both these components were integrated into a first-person shooter game developed from the scratch which has thirty sections. The MLP neural network developed uses the game parameters to predict the difficulty level of the next section, and then feed the results into the fuzzy engine. After that, the fuzzy engine incorporates the game state parameters and the difficulty value with the knowledge base and computes an adequate difficulty adjustment. Finally, an experiment was conducted to compare the capabilities of the DDA integrated game with the traditional game. Compared to the base game, the developed DDA integrated game improved the players ability to finish the game by 30% and the game reduced the number of failed attempts by 76%.


Dissanayake, T.; Rajapaksha, Y.; Ragel, R.; Nawinne, I. An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition. Sensors 2019, 19, 4495.
Tags: Machine learning and Data Mining