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Map Generator and Path Search in a Map for the Strategy Games
Karásek, Štěpán ; Mráček, Štěpán (referee) ; Orság, Filip (advisor)
This thesis consists of two parts. The first part deals with procedural map generation aiming at terrain generation. For the most part, the bases of terrain generation, usable algorithms and the ways of generating other landscape elements are followed up. A terrain generator using diamond-square algorithm is then implemented and integrated into the existing game engine. The second part of the thesis analyses the pathfinding problem, describes usable algorithms and one of them in detail. Furthemore, the post-processing of the path is being solved and possibilities of hierarchical pathfinding algorithms are presented. Pathfinding using A* algorithm is also implemented and integrated into existing game engine. Finally, functioanality of both the algorithms is demonstrated in an interactive application.
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Neural Networks and Genetic Algorithm
Karásek, Štěpán ; Snášelová, Petra (referee) ; Zbořil, František (advisor)
This thesis deals with evolutionary and genetic algorithms and the possible ways of combining them. The theoretical part of the thesis describes genetic algorithms and neural networks. In addition, the possible combinations and existing algorithms are presented. The practical part of this thesis describes the implementation of the algorithm NEAT and the experiments performed. A combination with differential evolution is proposed and tested. Lastly, NEAT is compared to the algorithms backpropagation (for feed-forward neural networks) and backpropagation through time (for recurrent neural networks), which are used for learning neural networks. Comparison is aimed at learning speed, network response quality and their dependence on network size.
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Neural Networks and Genetic Algorithm
Karásek, Štěpán ; Snášelová, Petra (referee) ; Zbořil, František (advisor)
This thesis deals with evolutionary and genetic algorithms and the possible ways of combining them. The theoretical part of the thesis describes genetic algorithms and neural networks. In addition, the possible combinations and existing algorithms are presented. The practical part of this thesis describes the implementation of the algorithm NEAT and the experiments performed. A combination with differential evolution is proposed and tested. Lastly, NEAT is compared to the algorithms backpropagation (for feed-forward neural networks) and backpropagation through time (for recurrent neural networks), which are used for learning neural networks. Comparison is aimed at learning speed, network response quality and their dependence on network size.
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Map Generator and Path Search in a Map for the Strategy Games
Karásek, Štěpán ; Mráček, Štěpán (referee) ; Orság, Filip (advisor)
This thesis consists of two parts. The first part deals with procedural map generation aiming at terrain generation. For the most part, the bases of terrain generation, usable algorithms and the ways of generating other landscape elements are followed up. A terrain generator using diamond-square algorithm is then implemented and integrated into the existing game engine. The second part of the thesis analyses the pathfinding problem, describes usable algorithms and one of them in detail. Furthemore, the post-processing of the path is being solved and possibilities of hierarchical pathfinding algorithms are presented. Pathfinding using A* algorithm is also implemented and integrated into existing game engine. Finally, functioanality of both the algorithms is demonstrated in an interactive application.
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