Multimedia Tools and Applications

, Volume 45, Issue 1–3, pp 247–261 | Cite as

A DR algorithm based on artificial potential field method

  • Xiang-Bin ShiEmail author
  • Xue Wang
  • Jing Bi
  • Fang Liu
  • Dan Yang
  • Xian-Yan Liu


Considering player entity’s motion regularity into DR (Dead Reckoning) algorithm can improve its prediction accuracy in MMOG (Massively Multiplayer Online Games), a novel DR algorithm was proposed to solve this problem in this paper. First the artificial potential field model of player entities is created, and then the acceleration of player entities is weighted with the acceleration produced by the potential field force and the acceleration reckoned by the traditional DR algorithm. In order to calculate the weight, Q-Learning algorithm is used. The experiments show that the method can improve prediction accuracy and reduce the network traffic.


MMOG Dead reckoning algorithm Artificial potential field Q-Learning 



This paper was supported by a grant from the Natural Science Foundation of Liaoning Province of China (20052007) and Foundation of Liaoning Educational Committee (2004D116).

Special thanks to colleagues in our lab helped us in experimental design.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Xiang-Bin Shi
    • 1
    • 2
    Email author
  • Xue Wang
    • 2
  • Jing Bi
    • 1
  • Fang Liu
    • 1
  • Dan Yang
    • 2
  • Xian-Yan Liu
    • 2
  1. 1.Department of ComputerShenyang Institute of Aeronautical EngineeringShenyangChina
  2. 2.School of Information Science and TechnologyLiaoning UniversityShenyangChina

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