Research on Intelligent Game Character through Performance Enhancements of Physics Engine in Computer Games


The KIPS Transactions:PartB , Vol. 13, No. 1, pp. 15-20, Feb. 2006
10.3745/KIPSTB.2006.13.1.15,   PDF Download:

Abstract

This paper describes research on intelligent game character through performance enhancements of physics engine in computer games. The algorithm that recognizes the physics situation uses momentum back-propagation neural networks. Also, we present an experiment and its results, integration methods that display optimum performance based on the physics situation. In this experiment on integration methods, the Euler method was shown to produce the best results in terms of fps in a simulation environment with collision detection. Simulation with collision detection was shown similar fps for all three methods and the Runge-kutta method was shown the greatest accuracy. In the experiment on physics situation recognition, a physics situation recognition algorithm where the number of input layers (number of physical parameters) and output layers (destruction value for the master car) is fixed has shown the best performance when the number of hidden layers is 3 and the learning count number is 30,000. Since we tested with rigid bodies only, we are currently studying efficient physics situation recognition for soft body objects.


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Cite this article
[IEEE Style]
J. H. Choi, D. K. Shin, D. I. Shin, "Research on Intelligent Game Character through Performance Enhancements of Physics Engine in Computer Games," The KIPS Transactions:PartB , vol. 13, no. 1, pp. 15-20, 2006. DOI: 10.3745/KIPSTB.2006.13.1.15.

[ACM Style]
Jong Hwa Choi, Dong Kyoo Shin, and Dong Il Shin. 2006. Research on Intelligent Game Character through Performance Enhancements of Physics Engine in Computer Games. The KIPS Transactions:PartB , 13, 1, (2006), 15-20. DOI: 10.3745/KIPSTB.2006.13.1.15.