Optimizing Player Satisfaction
Papers from the 2007 AIIDE Workshop
John Hallam and Georgios Yannakakis, Cochairs
The current state-of-the-art in intelligent game design using AI techniques is mainly focused on generating humanlike and intelligent characters. Although 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 little 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 game-play experiences for the player. We want to encourage dialog among researchers in AI, human-computer interaction, affective computing and psychology disciplines who investigate dissimilar methodologies for improving user (player) experiences. This workshop should yield an understanding of state-of-the-art approaches for capturing and augmenting player satisfaction in games.