Towards Capturing and Enhancing Entertainment in Computer Games

  • Georgios N. Yannakakis
  • John Hallam
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3955)


This paper introduces quantitative measurements/metrics of qualitative entertainment features within computer game environments and proposes artificial intelligence (AI) techniques for optimizing entertainment in such interactive systems. A human-verified metric of interest (i.e. player entertainment in real-time) for predator/prey games and a neuro-evolution on-line learning (i.e. during play) approach have already been reported in the literature to serve this purpose. In this paper, an alternative quantitative approach to entertainment modeling based on psychological studies in the field of computer games is introduced and a comparative study of the two approaches is presented. Artificial neural networks (ANNs) and fuzzy ANNs are used to model player satisfaction (interest) in real-time and investigate quantitatively how the qualitative factors of challenge and curiosity contribute to human entertainment. We demonstrate that appropriate non-extreme levels of challenge and curiosity generate high values of entertainment and we discuss the extensibility of the approach to other genres of digital entertainment and edutainment.


Computer Game Fuzzy Rule Human Judgement Membership Function Parameter Opponent Behavior 
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

  • Georgios N. Yannakakis
    • 1
  • John Hallam
    • 1
  1. 1.Maersk Institute for production TechnologyUniversity of Southern DenmarkOdense MDenmark

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