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:
Player Satisfaction Workshop (Technical!)