Interest modeling in games: the case of dead reckoning

In games, the goals and interests of players are key factors in their behavior. However, techniques used by networked games to cope with infrequent updates and message loss, such as dead reckoning, estimate a player’s movements based mainly on previous observations. The estimations are typically made by using dynamics of motion, taking only inertia and some external factors (e.g., gravity, wind) into account while completely ignoring the player’s goals (e.g., chasing other players or collecting objects). This paper proposes AntReckoning: a dead reckoning algorithm, inspired from ant colonies, which models the players’ interests to predict their movements. AntReckoning incorporates a player’s interest in specific locations, objects, and avatars in the equations of motion in the form of attraction forces. In practice, these points of interest generate pheromones, which spread and fade in the game world, and are a source of attraction. To motivate and validate our approach we collected traces from Quake III. We conducted specific experiments that demonstrate the effect of game-related goals, map features, objects, and other players on the mobility of avatars. Our simulations using traces from Quake III and World of Warcraft show that AntReckoning improves the accuracy by up to 44 % over traditional dead reckoning techniques and can decrease the upload bandwidth by up to 32 %.