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Artificial Intelligence for Game Playing
Bayer, Václav ; Škoda, Petr (referee) ; Smrž, Pavel (advisor)
The focus of this work is the use of artificial intelligence methods for a playing of real-time strategic (RTS) games, where all interactions of players are performed in real time (in parallel). The thesis deals mainly with the use of machine learning method Q-learning, which is based on reinforcement learning and Markov decision process. The practice part of this work is implemented for StarCraft: Brood War game.A proposed solution learns to make up an optimal order of buildings construction in respect to a playing style (strategy) of the opponent(s). The solution is proposed within the rules of the SSCAIT tournament. Analysis and evaluation of the proposed system are based on a comparison with other participants of the competition as well as a comparison of the system behavior before and after the playing of a set of the games.
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Quality control of interest education in the Ústí nad Labem region
Bayer, Václav ; Trunda, Jiří (advisor) ; Paulovčáková, Lucie (referee)
Interest-based education and leisure time are important in every person's life and they influence the proper functioning of the whole society. In order to fulfil its functions, it is important to emphasize its quality. The aim of the Diploma Thesis The Management of Interest Based Education Quality in the Ústí Region is to reveal the real status of the quality management of the interest-based education in the leisure time centres; and to identify the possibilities to innovate the leisure time education quality management. The questionnaire survey was used as the main research method of data collection. Interviews were used as the supplementary method. KEYWORDS interest-based education, quality management, leisure centres, quality
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Artificial Intelligence for Game Playing
Bayer, Václav ; Škoda, Petr (referee) ; Smrž, Pavel (advisor)
The focus of this work is the use of artificial intelligence methods for a playing of real-time strategic (RTS) games, where all interactions of players are performed in real time (in parallel). The thesis deals mainly with the use of machine learning method Q-learning, which is based on reinforcement learning and Markov decision process. The practice part of this work is implemented for StarCraft: Brood War game.A proposed solution learns to make up an optimal order of buildings construction in respect to a playing style (strategy) of the opponent(s). The solution is proposed within the rules of the SSCAIT tournament. Analysis and evaluation of the proposed system are based on a comparison with other participants of the competition as well as a comparison of the system behavior before and after the playing of a set of the games.
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