Player Satisfaction Workshop (Technical!)

October 1, 2006

Oct 1st
Rome, Italy
The current state-of-the-art in intelligent game design using AI techniques is mainly focused on generating human-like and intelligent characters. Even though complex behaviors emerge through various adaptive learning techniques, there is generally little further analysis of whether these behaviors contribute to the satisfaction of the player. The implicit hypothesis motivating this research is that intelligent opponent behaviors enable the player to gain more satisfaction from the game. This hypothesis may well be true; however, since no notion of entertainment or enjoyment is explicitly defined, there is therefore few evidence that a specific opponent behavior generates enjoyable games.
The focus of this workshop is on adaptive methodologies based on richer forms of human-machine interaction for augmenting gameplay experiences for the player. We want to encourage dialog among researchers in AI, human-computer interaction and psychology disciplines who investigate dissimilar methodologies for improving gameplay experiences. This workshop should yield an understanding of state-of-the-art approaches for capturing and augmenting player satisfaction in computer and physical (interactive) games.
Topics relevant to this workshop include, but are not limited to, the following areas:

  • Adaptive learning for entertainment augmentation.
  • Empirical approaches to entertainment modeling in games.
  • Psychological approaches to entertainment capture / Psychology of entertainment.
  • Player modeling approaches for optimizing entertainment.
  • Usability (entertainment) testing for adaptive game design.
  • Player-Game Interaction through biosignals.