Game Real-Time Strategy dengan menggunakan Artificial Intelligence Quantified Judgement Model dan Backpropagation Neural Network
Real-Time Strategy (RTS) Game is a quite popular video game genre. The uniqueness of RTS Games is that it is a Strategy Game where time will still continue for all the players. This creates situations where the player must determine their strategies in a matter of seconds. To get a good gameplay experience, then we would need enemies for the player. The way we could do that is to create an AI that could take into account the mechanics of the game. This thesis is aimed to broaden our knowledge on how to develop AI for RTS games.
The game will be created in Unity Game Engine 5.1.2f1, where the AI that is going to be implemented is the Quantified Judgement Model and the Neural Network backpropagation. The Quantified Judgement Model will act as Abstract Controller, giving orders to his troops much like a general in a war. Neural Network Backpropagation will be used for the Virtual Character, where the AI will act for each of the troops to think on what they should do. Whether they have to fall back, or keep going forward according to the orders given to him.
Desmond, M. S.. 2007 Implementation of The Quantified Judgement Model to examine the impact of Human Factors on Marine Corps Distributed Operations. Thesis. Naval Post-Graduate School 2007.
Dickheiser, M.. 2006. Game Programming Gems 6 (Game Development Series). Charles River Media, Inc..
Ding, M., Wang, L., & Bi, R.. 2001. An ANN-based Approach for Forecasting the Power Output of Photovoltaic System. Procedia Environmental Sciences 11, 2011. 1308 – 1315.
Hajek, M. 2005. Neural Networks. South Africa: University of KwaZulu-Natal
Metoyer, R.,et. al.. 2010. Explaining how to play real-time strategy games. Knowledge-Based Systems, 23 295–301.
Murias, K., et. al.. 2016. The Effects of Video Game Use on Performance in a Virtual Navigation Task. Computers in Human Behavior, 2016. 398-406.
Qun, Dai. 2014. A two-phased and Ensemble scheme integrated Backpropagation algorithm. Applied Soft Computing 24. 1124–1135.
Rhalibi, A. E., Wong, K. W., & Price, M.. 2008. Artificial Intelligence for Computer Games. International Journal of Computer Games Technology. Hindawi Publishing Corporation.
Robertson, G., & Watson, I. D. 2014. A Review of Real-Time Strategy Game AI. AI Magazine, 35(4), 75-104.
Sanchez, D., Dalmau, C.. 2004. Core Techniques and Algorithm in Game Programming. Indiana: New Riders Publishing.
Shanin, M. A.. 2014. State-of-the-art Review of Some Artificial Intelligence. Applications in Pile Foundations. Geoscience Frontiers 7, 2016. 33-44.
Stanney, K. M., Mourant, R. R., Kennedy, R. S.. 1998. Human factors issues in virtual environments: are view of the literature. Presence Teleoperators Virtual Environ.7, 327–351.
Takizawa, H. & Chida, Tatsuya. 2009. Evaluating Computational Performance of Backpropagation Learning on Graphics Hardware. Electronic Notes in Theoretical Computer Science, 2009. 379–389.
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