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Manager for Board Games
Kouřil, Jan ; Zbořil, František (referee) ; Rozman, Jaroslav (advisor)
The main goal of this bachelor work was to remove errors from actual programming, to implement possibility of arranging different forms of tournaments and to add support for games gomoku, checkers, chess and go. It was also necessary to programm possibility of human play for better debugging of artificial intelligences, which are playing against each other in this manager. As a minor goal i would mention implementation of standard protocol in game gomoku, which supports connection for universal artificial intelligences. From begginning I discuss theoretical solutions of given problem, board games, their rules and algorithm proposal. Next, there is a summary of implementation and its realization. In the end posible extentions of manager are mentioned.
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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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Graphical Interface for Board Games on Android Platform
Káčerik, Martin ; Láník, Aleš (referee) ; Polok, Lukáš (advisor)
The bachelor's thesis focuses on visual representation of three-dimensional set-up of board game. It describes the possibilities of the visualization of three-dimensional graphics on the Android platform. It deals with principles of advanced rendering methods. The final outcome of the thesis is Android application, which serves as a manager of modules containing implementation of board games. Games are projected using the ray-tracing algorithm.
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Manager for Board Games
Kusák, Jiří ; Jurka, Pavel (referee) ; Rozman, Jaroslav (advisor)
The main goal of this work is to design and realize an application for managing two programs playing against each other. At the beginning I'm discussing board games (especially their rules) and player's skill rating systems. Then there is short analysis of the actual situation in this area. Fundamental part of the work is the description of the designing and realization of the manager. After that there is short description how to control the application. In the end I'm evaluating achieved results and proposing additional directions for the program development.
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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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Czech Secondhand Board Game Price Analysis
Pospíšil, Matyáš ; Polák, Petr (advisor) ; Bajgar, Matěj (referee)
This thesis explores the various factors a ecting the willingness to pay (WTP) for secondhand board games in the Czech market. A regression analysis was em- ployed to examine the influence of age, the importance of reviews and themes, monthly income and media influence on consumers' WTP. The study also dif- ferentiates between more and less complex games to highlight subtle shifts in WTP based on game complexity. The empirical results provide insights into the baseline for the discounted price, the significant negative e ect that age or inexperience in buying a board game can have and the remarkable positive ef- fect that following board game media has on the price. While the study centres on the Czech market, its findings o er practical implications for the broader secondhand board game market and retailers. JEL Classification C50, D12, D49, L14, L15 Keywords Secondhand Market, Board Games, Willingness to Pay, Regression Analysis Title Czech Secondhand Board Game Price Analysis Author's e-mail pospisil.matyas@gmail.com Supervisor's e-mail polakpet@gmail.com
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Deep Learning Methods for Machine Playing the Scotland Yard Board Game
Hrkľová, Zuzana ; Janoušek, Vladimír (referee) ; Zbořil, František (advisor)
Táto práca sa zaoberá metódami hlbokého učenia, ktoré sú aplikovateľné na stolné hry s neurčitosťou. V rámci práce boli naštudované princípy učenia s posilňovaním, s hlavným zameraním na Q-learning algoritmy, spomedzi ktorých bol vybraný Deep Q-Network algoritmus. Ten bol následne implementovaný na zjednodušených pravidlách stolnej hry Scotland Yard. Konečná implementácia bola porovnaná s metódami Alpha-Beta a Monte Carlo Tree Search. S výsledkov vyplinulo, že schovávaný hráč riadený DQN algoritmom predstavoval pre ostatné metódy najťažšieho protihráča, narozdiel od hľadajúcich hráčov, ktorým sa nepodarilo zlepšiť existujúce riešenia. Napriek tomu, že implementovaná metóda nedosiahla lepšie výsledky oproti doposiaľ existujúcim metódam, ukázalo sa, že potrebuje najmenej výpočetných zdrojov a času na vykonanie daného ťahu. To ju robí najperspektívnejšou zo spomínaných metód na budúcu možnú implementáciu originálnej verzie danej hry.
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