Game Real-Time Strategy dengan menggunakan Artificial Intelligence Quantified Judgement Model dan Backpropagation Neural Network

Owen Ener, Gregorius Satia Budhi, Liliana Liliana


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.


Real-Time Strategy Game, Artificial Intelligence, Quantified Judgement Model, Neural Network Backpropagation.

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