Universal Algorithm for Creating A Small Scale Reusable Simulation Data in Real-time Strategy Games

Real-time strategy games are of such high complexity that consideration of trying to brute force all actions and states is not only impractical, but impossible. Approximations, information abstractions, and models are, therefore, the necessity when creating game bots that play this genre of games. To create such bots, the detailed data is needed to base them on. This article introduces a universal algorithm that creates reusable simulation data of one attacking unit on a building and tests the feasibility of doing such a task. This paper concludes that capturing all relevant data in a sub-segment of real-time strategygames is feasible. Gathered data holds valuable information and can be reused in new research without the need of repeating the simulations

Combat Management in Starcraft II Game by Means of Artificial Intelligence

Táto práca sa zaoberá využitím umelej inteligencie a návrh funkčného modulu pre strategickú hru StarCraft II. Riešenie využíva neurónové siete a Q-learning pre boj. Pre implementáciu systému a jej prepojenie s hrou StarCraft používam StarCraft 2 Learning Environment. Vyhodnotenie systému je založené na jej schopnosti vykonať pokrok.This thesis focuses on the use of Artificial Intelligence and design of working module in Real-Time Strategy (RTS) game, StarCraft II.  The proposed solution uses Neural Network and Q-learning for combat management. For implementation, the StarCraft 2 Learning Environment has been used as a means of communication between the designed system and the game. Evaluation of the system is based on its ability to make progress over time.