Static and Dynamic Difficulty Level Design for Edutainment Game Using Artificial Neural Networks

  • Kok Wai Wong
  • Chun Che Fung
  • Arnold Depickere
  • Shri Rai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3942)


When designing a game, one of the major tasks is to design a game of exciting and challenging difficulty levels to maintain the interest level of a player throughout the game. This is especially important when designing an educational game. This paper proposes the use of Artificial Neural Networks (ANNs), specifically the Backpropagation Neural Networks (BPNNs) for handling the gaming experience. The BPNNs can provide targeted learning experience for the user or the student. This will achieve personalized learning that is an important issue for student relationship management. The proposed frameworks will provide motivation for the student as the difficulty level progresses and adjusts to suit individual users.


Data Warehouse Customer Relationship Management Difficulty Level Game Design Educational Game 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kok Wai Wong
    • 1
  • Chun Che Fung
    • 1
  • Arnold Depickere
    • 2
  • Shri Rai
    • 1
  1. 1.School of Information TechnologyMurdoch UniversityMurdoch
  2. 2.Division of ArtsMurdoch UniversityMurdoch

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