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Arimaa
Kanis, Martin ; Škoda, Petr (referee) ; Smrž, Pavel (advisor)
Arimaa is a board game with simple rules. It is simple for human but at the same time difficult for computers. The aim of this bachelor thesis is to familiarize with game playing methods with features of the artificial intelligence. The next aim is to design and implement the program, that would be able to play against others players and programs. The most important features of the program are move generation, search and evaluation of positions. At the end, the program was tested on the game server, where played against others programs.
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Demonstrational Program for IZU Course
Hreha, Tomáš ; Šůstek, Martin (referee) ; Zbořil, František (advisor)
This bachelor thesis deals with the design of application for visualization of fundamental algorithms of artificial intelligence. The first part describes theoretical part of implemented topics and methods, next part briefly describes used technologies, reasons why they were used and their practical usage in context of result application. The next part is dedicated to user interface, its main components and describes ways how application interacts with user and how user can interact with application. The last part contains comparison with original demo applications and summarize results of application testing.
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Strategic Game with Uncertainity
Gerža, Martin ; Zbořil, František (referee) ; Zbořil, František (advisor)
This thesis focuses on the implementation of a system for playing the board game Scotland Yard autonomously and also focuses on a comparison of this system with similar ones. I focused on obtaining enough information about the possible methods that should be suitable for such a system and decided to implement this system using the Monte Carlo Tree Search method. The result implementation of the system was tested against similar systems, achieving an excellent result against another system that used an equivalent method. There was achieved a balanced result against a system that used the Alpha-Beta method. The main result of this work is a working version of an autonomous system for playing the game Scotland Yard on a reduced field. It also provides the possibility of using two similar systems within a single program in order to compare their implementations.
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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
Gerža, Martin ; Zbořil, František (referee) ; Zbořil, František (advisor)
This thesis focuses on the implementation of a system for playing the board game Scotland Yard autonomously and also focuses on a comparison of this system with similar ones. I focused on obtaining enough information about the possible methods that should be suitable for such a system and decided to implement this system using the Monte Carlo Tree Search method. The result implementation of the system was tested against similar systems, achieving an excellent result against another system that used an equivalent method. There was achieved a balanced result against a system that used the Alpha-Beta method. The main result of this work is a working version of an autonomous system for playing the game Scotland Yard on a reduced field. It also provides the possibility of using two similar systems within a single program in order to compare their implementations.
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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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Demonstrational Program for IZU Course
Hreha, Tomáš ; Šůstek, Martin (referee) ; Zbořil, František (advisor)
This bachelor thesis deals with the design of application for visualization of fundamental algorithms of artificial intelligence. The first part describes theoretical part of implemented topics and methods, next part briefly describes used technologies, reasons why they were used and their practical usage in context of result application. The next part is dedicated to user interface, its main components and describes ways how application interacts with user and how user can interact with application. The last part contains comparison with original demo applications and summarize results of application testing.
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Arimaa
Kanis, Martin ; Škoda, Petr (referee) ; Smrž, Pavel (advisor)
Arimaa is a board game with simple rules. It is simple for human but at the same time difficult for computers. The aim of this bachelor thesis is to familiarize with game playing methods with features of the artificial intelligence. The next aim is to design and implement the program, that would be able to play against others players and programs. The most important features of the program are move generation, search and evaluation of positions. At the end, the program was tested on the game server, where played against others programs.
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Automatic System for Starcraft Game Playing
Skácel, Dalibor ; Škoda, Petr (referee) ; Smrž, Pavel (advisor)
This thesis focuses on artificial intelligence principles used in komputer games and their demonstration by implemented automatic system for playing Starcraft. This system uses methods such as: decision trees, finite state machines and it handles decision making in real time environment, control of multiple units with different abilities and works with partial informatik about opponent. Starcraft: Broodwar is one of the most famous real time strategy games and it was chosen for this thesis for its balance and available application interface for controlling the game. The goal is to show artificial intelligence methods in praxis and to create a system which is competitive against other systems in SSCAI (Student Starcraft AI Tournament) competition and even against human players.
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