Dynamic Difficulty Adjustment for Racing Multiplayer Games Using Reinforcement Learning Algorithm
Publish Year: 1395
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CGCO02_006
تاریخ نمایه سازی: 19 خرداد 1396
Abstract:
Multiplayer video games are well considered among the players, since players can test their abilities with other players, proof themselves, and enjoy playing with real players. Whenever players with different level of skills play a game with other players, adjusting the difficulty level of the game will be crucial. The excitement of a game would decrease, if the game appears to be easy for some players, while it is very difficult for the others. Since in multiplayer games players interact with each other, dynamic difficulty adjustment is a challenging problem. Generally, difficulty adjustment techniques are proposed for single player games. This paper proposes an automatic system for difficulty adjustment of a multiplayer car racing game using Reinforcement Learning (RL). The results of the user study conducted on this system show the effectiveness of using this module
Authors
Erfan Pirbabaei
M.A. Student in Production of Computer Games, Faculty of Multimedia, Tabriz Islamic Art University, Tabriz, Iran
Hesam Sakian
M.A. Student in Intelligent Simulator Design, Faculty of Multimedia, , Tabriz Islamic Art University, Tabriz, Iran
Younes Sekhavat
Assistant professor, Faculty of Multimedia, Tabriz Islamic Art University, Tabriz, Iran