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Playing the Card Game Gwen by Computer
Majer, Jakub ; Šátek, Václav (referee) ; Zbořil, František (advisor)
Táto práca implementuje a hodnotí Monte Carlo Tree Search AI agenta v rámci strategickej kartovej hry Gwent. Skúmajú sa teoretické základy hry Gwent a MCTS algoritmu, pričom sa zameriava skôr na praktickú aplikáciu ako na teoretické príspevky. Výkon agenta MCTS sa testuje proti ľudským hráčom a iným AI protivníkom, čím sa demonštruje jeho účinnosť pri výbere správnych stratégií a rozhodovaní. Výsledky naznačujú potenciál MCTS pri vývoji sofistikovaných AI agentov pre strategické kartové hry, ako je Gwent.
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Hraní hry The Duke počítačem
Horváth, Adrián ; Veigend, Petr (referee) ; Zbořil, František (advisor)
This thesis deals with the analysis of game strategies in the game "The Duke" using artificial intelligence (AI) algorithms. We compare three different approaches: minimax, alpha-beta pruning and Monte Carlo Tree Search (MCTS). We study the rules of the game, identify key factors affecting strategy, and perform an experimental comparison of the algorithms’ results. We summarize the results and discuss future research directions in game AI.
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Agent Based Gameplaying System
Trutman, Michal ; Zbořil, František (referee) ; Král, Jiří (advisor)
This thesis deals with general game playing agent systems. On the contrary with common agents, which are designed only for a specified task or a game, general game playing agents have to be able to play basically any arbitrary game described in a formal declarative language. The biggest challenge is that the game rules are not known beforehand, which makes it impossible to use some optimizations or to make a good heuristic function. The thesis consists of a theoretical and a practical part. The first part introduces the field of general game playing agents, defines the Game Description Language and covers construction of heuristic evaluation functions and their integration within the Monte Carlo tree search algorithm. In the practical part, a general method of creating a new heuristic function is presented, which is later integrated into a proper agent, which is compared then with other systems.
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Playing Gomoku with Neural Networks
Slávka, Michal ; Kolář, Martin (referee) ; Hradiš, Michal (advisor)
Táto práca sa zaoberá použitím algoritmu AlphaZero pre hru Gomoku. AlphaZero je založený na spätnoväzbnom učení a k trénovaniu nemusia byť využité žiadne existujúce datasety. Trénovanie prebieha iba na hrách algoritmu samého so sebou. AlphaZero používa algoritmus na prehľadávanie stromu, pre zlepšenie stratégie. Na vylepšnej stratégii sa následne trénuje neurónová sieť. Tento prístup bol úspešný v hrách proti existujúcim algoritmom. Generovanie trénovacích dát vysokej kvality si vyžaduje veľa výpočetne náročných iterácií trénovania a generovania dát. Experimenty ukázali, že každou iteráciou sa algoritmus zlepšuje, čo naznačuje, že je ešte miesto na zlepšenie, ale množstvo iterácií nedostačovalo na to, aby bol poriadne natrénovaný.
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Algorithms for Tafl Games
Halmo, Kryštof ; Kočí, Radek (referee) ; Zbořil, František (advisor)
The goal for this work is to create a program witch allows the possibility to play some types of Tafle games against a some algorithms or against a other player. Algorithms used in the solution wear MCTS, Alfabeta and Minmax whit heurystyks which help to find a move faster and evaluate the game board whit specified parameters. Created solution allows the user to select the parameters for running this program. Results of this work provides a comparison between different types of Tafle games and the comparison of different types of algorithms agents one another.
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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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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 was 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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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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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 was 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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