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Artificial Intelligence for Children of the Galaxy Computer Game
Šmejkal, Pavel ; Gemrot, Jakub (advisor) ; Trunda, Otakar (referee)
Even though artificial intelligence (AI) agents are now able to solve many classical games, in the field of computer strategy games, the AI opponents still leave much to be desired. In this work we tackle a problem of combat in strategy video games by adapting existing search approaches: Portfolio greedy search (PGS) and Monte-Carlo tree search (MCTS). We also introduce an improved version of MCTS called MCTS considering hit points (MCTS_HP). These methods are evaluated in context of a recently released 4X strategy game Children of the Galaxy. We implement a combat simulator for the game and a benchmarking framework where various AI approaches can be compared. We show that for small to medium combat MCTS methods are superior to PGS. In all scenarios MCTS_HP is equal or better than regular MCTS due to its better search guidance. In smaller scenarios MCTS_HP with only 100 millisecond time limit outperforms regular MCTS with 2 second time limit. By combining fast greedy search for large combats and more precise MCTS_HP for smaller scenarios a universal AI player can be created.
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Artificial Intelligence for Quoridor Board Game
Brenner, Matyáš ; Gemrot, Jakub (advisor) ; Černý, Martin (referee)
The aim of this work is to design an Artificial Intelligence for Sector 66, which is a board game based on Quoridor. In Sector 66 there is a possibility to use spells and fields with some special effects. The Artificial Intelligence is based on Monte Carlo Tree Search. It can be used for 2 to 4 players. The Artificial Intelligence introduced in this work can work with the high branching factor of Quoridor/Sector 66 Game and can also handle unknown elements represented by user defined plug-ins. The game and the Artificial Intelligence has been developed using .NET Framework, XNA and C#. Powered by TCPDF (www.tcpdf.org)
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Distributed Monte-Carlo Tree Search for Games with Team of Cooperative Agents
Filip, Ondřej ; Lisý, Viliam (advisor) ; Majerech, Vladan (referee)
The aim of this work is design, implementation and experimental evaluation of distributed algorithms for planning actions of a team of cooperative autonomous agents. Particular algorithms require different amount of communication. In the work, the related research on Monte-Carlo tree search algorithm, its parallelization and distributability and algorithms for distributed coordination of autonomous agents. Designed algorithms are tested in the environment of the game of Ms Pac-Man. Quality of the algorithms is tested in dependence on computational time, the amount of communication and the robustness against communication failures. Particular algorithms are compared according to these characteristics. Powered by TCPDF (www.tcpdf.org)
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