臺灣博碩士論文加值系統

本文主要的研究目的在於藉由使用高階資訊的方式，以模糊邏輯的方式實現，將其應用在模擬汽車控制器上，讓控制器上面的人工智慧能夠以接近人類的思考方式做出恰當的反應。
高階資訊藉由控制器本身的偵測器偵測前方賽道，經模糊系統轉換成賽道中距離前方彎道的距離和目標彎道的極緩程度等資訊，控制器再利用這些資訊思考策略並決定速度、入彎位置、出彎方式…等等，作出近似人類思考模式的駕駛行為。

The main idea of thesis is that by using high-level information implemented by fuzzy logic based simulated car controller. The artificial of intelligence of controller can make proper decision or reaction based on the high-level information.
The high-level information obtained by sensor detecting the distance between car and track, the fuzzy system transform this information into distance from the corner and the sharpness of the corner, then the controller planning to drive with the target speed and the proper position.

摘要 I
Abstract II
誌謝 III
目錄 IV
圖例 VI
表格 VII
第一章 簡介 1
1.1 研究目的 1
1.2 論文架構 2
第二章 文獻縱覽 3
2.1 玩家模擬 3
2.2 軌跡最佳化 8
2.3 基底架構 8
第三章 TORCS與AI基底架構 10
3.1 TORCS(The Open Racing Car Simulator) 10
3.2 AI引擎之基底架構 14
第四章 研究方法 21
4.1 彎道距離與急緩度 21
4.2 模糊系統和入彎控制模組 24
第五章 實驗結果 28
5.1 軌跡與成績比較 28
第六章 結論與未來展望 34
參考文獻 35

[1] Togelius, J.; De Nardi, R.; Lucas, S.M.,”Towards automatic personalised content creation for racing games,”Proc. CIG (Computational Intelligence in Games),pp.252-259,2010.
[2] Togelius, J.,”Optimization, Imitation and Innovation:Computational Intelligence and Games,”PhD Thesis, University of Essex,2007.
[3] Togelius, J., De Nardi, R. and Lucas, S.M.,”Making Racing Fun Through Player Modeling and Track Evolution,”In: Proceedings of the SAB’06 Workshop on Adaptive Approaches for Optimizing Player Satisfaction in Computer and Physical Games,pp.61-70,2006
[4] Mu?瓨z, J.; Gutierrez, G.; Sanchis, A., ”A human-like TORCS controller for the Simulated Car Racing Championship,”Proc. CIG (Computational Intelligence in Games),pp.473-480,2010.
[5] Quadflieg, J.; Preuss, M.; Kramer, O.; Rudolph, G.,”Learning the Track and Planning Ahead in a Car Racing Controller,”Proc. CIG (Computational Intelligence in Games),pp.395-402,2010.
[6] Cardamone, L.; Loiacono, D.; Lanzi, P.L.,” Learning drivers for TORCS through imitation using supervised methods,”Proc. CIG (Computational Intelligence in Games),pp.148-155,2009.
[7] Cardamone, L.; Loiacono, D.; Lanzi, P.L.,” Evolving Competitive Car Controllers for Racing Games with neuroevolution,” Proc. GECCO (Proceedings of the 11th Annual conference on Genetic and evolutionary computation),pp.1179-1186,2009.
[8] Onieva, E.; Cardamone, L.; Loiacono, D.; Lanzi, P.L.,” Overtaking opponents with blocking strategies using fuzzy logic,” Proc. CIG (Computational Intelligence in Games),pp.123-130,2010
[9] Van Hoorn, N., Togelius, J., Wierstra, D. and Schmidhuber, J.,” Robust player imitation using multiobjective evolution,” In: Proceedings of the IEEE Congress on Evolutionary Computation,pp.652-659,2009
[10] Enrique Onieva, David A. Pelta, Vicente Milan?臃 and JoshueP?臆ez,”A fuzzy-rule-based driving architecture for non-player characters in a car racing game,” In: Soft Computing - A Fusion of Foundations, Methodologies and Applications,Volume 15,number 8,pp.1617-1629,2011
[11] Cardamone, L.; Loiacono, D.; Lanzi, P.L.,” Bardelli, A.P.; Searching for the Optimal Racing Line Using Genetic Algorithms,”Proc. CIG (Computational Intelligence in Games),pp.388-394,2010.
[12] " The open racing car simulator website.”[Online]. Available:http://torcs.sourceforge.net/