AntReckoning: Dead reckoning using interest modeling by pheromones

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 on previous observations only. The estimations are typically done using dynamics of motion, taking only inertia and 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 fade and spread in the game world, and are a source of attraction. Our simulations using mobility traces from World of Warcraft and Quake III show that AntReckoning improves the accuracy by up to 30% over traditional dead reckoning techniques.