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Solving Problems with Uncertainty
Hrdý, Libor ; Martinek, David (referee) ; Zbořil, František (advisor)
In this thesis is described implementation of the logical deskgame Backgammon, which is a game for two players, whereas one is substituted by computer. This thesis is focused on the problems of the programming the graphical user interface with help of toolkit WxWidgets and also the implemetnation of the game core (game controls and AI of the computer) by using ExpectMiniMax algorithm, that is used for the implementation of the games with the strong influence of random, games where random plays a big role, in this particular case throwing the cube.
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The Hnefatafl Board Game Artificial Intelligence
Stratilová, Lenka ; Doležel, Michal (referee) ; Kubát, David (advisor)
The main task of this bachelor thesis is to design, create and test the Hnefatafl board game artificial intelligence. In the beginning of thesis are description of Hnefatafl rules with its variations and the most common algorithms of artificial intelligence. The following describes the implementation and testing. The game allows two players mode, player against computer mode and two computers mode.
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Evolution Algorithm Used in Chess Game
Urminský, Andrej ; Straka, Martin (referee) ; Gajda, Zbyšek (advisor)
This thesis deals with a design of an evolutionary algorithm for an artificial intelligence in a chess game. This is accomplished by use of so called genetic algorithms. Java programming language and Eclipse, an open development platform, were used for implementation of this algorithm and the graphical user interface.
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Strategic Game with Uncertainity
Sova, Michal ; Zbořil, František (referee) ; Zbořil, František (advisor)
The thesis focuses on creating an autonomous system for the game Scotland Yard by using machine learning method. The problem is solved by algorithm Monte Carlo tree search. Algorithm Monte Carlo tree search was tested against algorithm Alpha-beta. These results showed that Monte Carlo tree search algorithm is operational but win rate of this algorithm is lower than win rate of algorithm Alpha-beta. The resulting system is functional, autonomous and capable of playing the game Scotland Yard on simplified game area. There was an attempt to expand simplified version of the game Scotland Yard. In expanded version algorithm Alpha-beta was not successful because of insufficient computational resources. Algorithm Monte Carlo tree search, on the other hand, was more successful in expanded version.
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Strategic Game with Uncertainity
Tulušák, Adrián ; Šimek, Václav (referee) ; Zbořil, František (advisor)
The thesis focuses on creating an autonomous functional system for the game Scotland Yard by using artificial intelligence methods for game theory and machine learning. The problem is solved by algorithm of game theory - Alpha Beta. There was an attempt to use machine learning, but it proved to be unsuccessful due to the large number of states for expansion and insufficient computational recourses. The solution using Alpha Beta algorithm was tested on human players and it proved the ability of artificial intelligence to fully compete against real players. The resulting system is functional, autonomous and capable of playing the game Scotland Yard on simplified game area. Based on these experiments, the thesis also introduces some improvements that could utilize machine learning and extend the existing solution.
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Playing the Board Game Stratego by Computer
Irovský, Dominik ; Šátek, Václav (referee) ; Zbořil, František (advisor)
The topic of this thesis is the board game of Stratego. This game features incomplete information. The goal of this thesis is research of existing game playing algorithms and, design and implementation of new solution. For the new solution modified version of Monte Carlo Tree Search as well as alfa-beta algorithm and expectimax were used. The solution was implemented as a console application with possibility of future expansion. Functionality of the solution was validated and tested using experiments. Effectivity of the final algorithm was satisfying
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Artificial intelligence in abstract 2-player games
Veselý, Pavel ; Valla, Tomáš (advisor) ; Baudiš, Petr (referee)
In this thesis we focus on algorithms for searching for the best move in a given position in an abstract strategy 2-player game. We describe algorithms Alpha-beta and Proof-number Search with their enhancements and we come with new ideas for making them quicker. We also propose an algorithm for choosing randomly between moves not much worse than the best move and ideas how to play in lost positions. We apply the algorithms on the game Tzaar which is special for having a lot of possible moves which makes the game hard for a computer. Our goal is to create a robot for playing Tzaar as good as possible. We show that our artificial intelligence can play on the level of best human and computer players. We also examine experimentally how enhancements of the algorithms help making computations quicker in this game.
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Strategic Game with Uncertainity
Sova, Michal ; Zbořil, František (referee) ; Zbořil, František (advisor)
The thesis focuses on creating an autonomous system for the game Scotland Yard by using machine learning method. The problem is solved by algorithm Monte Carlo tree search. Algorithm Monte Carlo tree search was tested against algorithm Alpha-beta. These results showed that Monte Carlo tree search algorithm is operational but win rate of this algorithm is lower than win rate of algorithm Alpha-beta. The resulting system is functional, autonomous and capable of playing the game Scotland Yard on simplified game area. There was an attempt to expand simplified version of the game Scotland Yard. In expanded version algorithm Alpha-beta was not successful because of insufficient computational resources. Algorithm Monte Carlo tree search, on the other hand, was more successful in expanded version.
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Strategic Game with Uncertainity
Tulušák, Adrián ; Šimek, Václav (referee) ; Zbořil, František (advisor)
The thesis focuses on creating an autonomous functional system for the game Scotland Yard by using artificial intelligence methods for game theory and machine learning. The problem is solved by algorithm of game theory - Alpha Beta. There was an attempt to use machine learning, but it proved to be unsuccessful due to the large number of states for expansion and insufficient computational recourses. The solution using Alpha Beta algorithm was tested on human players and it proved the ability of artificial intelligence to fully compete against real players. The resulting system is functional, autonomous and capable of playing the game Scotland Yard on simplified game area. Based on these experiments, the thesis also introduces some improvements that could utilize machine learning and extend the existing solution.
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