A DR algorithm based on artificial potential field method

Authors:
Xiang-Bin Shi;Xue Wang;Jing Bi;Fang Liu;Dan Yang;Xian-Yan Liu
Affiliations:
Department of Computer, Shenyang Institute of Aeronautical Engineering, Shenyang, China 110136 and School of Information Science and Technology, Liaoning University, Shenyang, China 110036;School of Information Science and Technology, Liaoning University, Shenyang, China 110036;Department of Computer, Shenyang Institute of Aeronautical Engineering, Shenyang, China 110136;Department of Computer, Shenyang Institute of Aeronautical Engineering, Shenyang, China 110136;School of Information Science and Technology, Liaoning University, Shenyang, China 110036;School of Information Science and Technology, Liaoning University, Shenyang, China 110036
Venue:
Multimedia Tools and Applications
Year:
2009

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Abstract

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.