In today’s gaming world, a player expects the same play experience whether playing on a local network or online with many geographically distant players on congested networks. Because of delay and loss, there may be discrepancies in the simulated environment from player to player, likely resulting in incorrect perception of events. It is desirable to develop methods that minimize this problem. Dead reckoning is one such method. Traditional dead reckoning schemes typically predict a player’s position linearly by assuming players move with constant force or velocity. In this paper, we consider team-based 2D online action games. In such games, player movement is rarely linear. Consequently, we implemented such a game to act as a test harness we used to collect a large amount of data from playing sessions involving a large number of experienced players. From analyzing this data, we identified play patterns, which we used to create three dead reckoning algorithms. We then used an extensive set of simulations to compare our algorithms with the IEEE standard dead reckoning algorithm and with the recent “Interest Scheme” algorithm. Our results are promising especially with respect to the average export error and the number of hits.
Traditional dead reckoning schemes predict a player's position by assuming that players move with constant force or velocity. However, because player movement is rarely linear in nature, using linear prediction fails to produce an accurate result. Among existing dead reckoning methods, only few focus on improving prediction accuracy via genuinely nontraditional methods for predicting the path of a player. In this paper, we propose a new prediction method based on play patterns. We implemented a 2D top-down multiplayer online game to act as a test harness that we used to collect play data from 44 experienced players. From the data for half of these players, we extracted play patterns, which we used to create our dead reckoning algorithm. A comparative evaluation proceeding from an extensive set of simulations (using the other half of our play data) suggests that our EKB algorithm yields more accurate predictions than the IEEE standard dead reckoning algorithm and the recent "Interest Scheme" algorithm.
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