Concealing jitter in Multi-Player Online Games through predictive behaviour modeling

Abstract

Network latency imposes a major hinderance on the responsiveness and consistency of a Multi-Player Online Game (MPOG). In past decades, several network topologies and latency handling solutions have been proposed and adopted in a variety of MPOGs. Another issue closely related to latency is jitter, which is caused by the variation of latency. Most of the existing MPOGs adopt a simplistic approach to tackling jitter: when one's varying latency exceeds a threshold, one will be forced to leave the game; otherwise latency is treated constant or estimated from historical data. However, forcing a player to quit in the middle of a game simply because of a spike of unusual lengthy latency has a significant negative impact on both the fairness of the game and the player's gameplay quality of experience (QoE). In this paper, we propose an alternative approach that instead conceals jitter by seamlessly and transparently switching between a remote human player and their intelligent agent that resembles them. To model such an intelligent agent, we further contribute a novel technique referred to as predictive modeling of user behaviour (PREMUB), which predicts an object's future state based on how the remote player interacts with the object in the past. We have also developed an online table tennis game to demonstrate this idea and compare the prediction accuracy between using PREMUB and using the existing technique of dead reckoning that does not consider a user's playing pattern.