Adaptive agent for player-specific fitness and health incentives in mobile location based games

As location-based mobile games become more popular, movement becomes an integral part of game play. This provides an opportunity for the game to influence player behavior in the real world, potentially inducing more physical activity (and better health) through intelligent adaptation of the game mechanic. We describe the application of Markov Decision Processes (MDPs) to model the player's behavior in a custom-built location-based zombie fighting game. The game agent uses this model-a user specific optimal policy (USOP)-to adjust the game behavior to encourage as much game play as possible. Our experiments with human subjects showed that game play time was indeed increased over the control condition. We look at how games can be used to model user behavior and then unobtrusively effect agent-determined behavioral change.