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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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Mastné kyseliny se stredne dlhým reťazcom v športovej výžive
Styková, Veronika
Abstract The first part of this diploma thesis "Medium Chain Fatty Acids in Sports Nutrition" focuses on a literature research of topics regarding rational nutrition and its history, the importance of fats in nutrition, a more detailed review of fats in sports nutrition. It also describes the impact of medium-chain fatty acids (MCTs) on the human body and on performance in sport. The practical part of the work focused on the design of a suitable methodology for the determination of fatty acids and the analysis of available MCT-containing dietary supplements by using gas chromatography. Based on the measurement results, the fatty acid profile of the samples was evaluated, a statistical evaluation was performed and the measurement results were compared with the nutritional information of the products as labelled on the product packaging. The diploma thesis also included a questionnaire that was designed for a general audience in order to determine the knowledge of supplements and the popularity of dietary supplements containing MCTs. The aim of this thesis was to provide as comprehensive an overview as possible of medium chain fatty acids and their impact in sports nutrition by using a various information sources, and to determine whether respondents had an overview of MCTs based on the questionnaire and to conduct an analysis of fatty acids in MCT-containing products.
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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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Genetic Algorithms driven by MCTS
Havránek, Štěpán ; Hric, Jan (advisor) ; Moudřík, Josef (referee)
Evolutionary and genetic algorithms are problem-solving methods designed according to a nature inspiration. They are used for solving hard problems that we cannot solve by any efficient specialized algorithm. The Monte Carlo method and its derivation the Monte Carlo Tree Search (MCTS) are based on sampling and are also commonly used for too complex problems, where we are dealing with enormous memory consumption and it is impossible to perform a complete searching. The goal of this thesis is to design a general problem solving method that is built from these two completely different approaches. We explain and implement the new method on one example problem: the Traveling salesman problem (TSP). Second part of this thesis contains various tests and experiments. We compare different settings and parametrizations of our method. The best performing variant is then compared with the classical evolutionary TSP solution or, for example, with greedy algorithms. Our method shows competitive results. The best results were achieved with the cooperation of our method and the classical evolutionary TSP solution. This union shows better results than any of its parts separately, which we find as a great success.
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Solving of Problems using MCTS
Malý, Dominik ; Hric, Jan (advisor) ; Majerech, Vladan (referee)
Title: Solving problems using MCTS Author: Dominik Malý Department: Department of theoretical informatics and mathematical logic Supervisor: RNDr. Jan Hric Supervisor's e-mail address: Jan.Hric@mff.cuni.cz Abstract: MCTS (Monte Carlo Tree Search) methods are a state-of-the-art approach to the computer solution of strategic board game Go. Because of their versatility and successfulness, these techniques show great potential for all kinds of problems. This paper aims to explore the suitability of MCTS for solving different kind of problems, specifically games of one player, like Sudoku or SameGame. I've created a computer player based on MCTS, who can solve not only Sudoku and SameGame, but also other tasks of similar kind. I've experimentally examined many MCTS extensions and their eligibility for solving these games and through extensive testing I've also compared the suitability of various kinds of UCT selection fun- ctions and used heuristics. In case of SameGame I've compared my algorithm to another exi- sting one undertaking the same problem. In the end I've described what kind of problems has a MCTS-based computer player to overcome, if it is to successfully solve games of this type, and what characteristics should these problems posses to be suitable for MCTS solution. Keywords: MCTS, Go, Sudoku,...
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A Controlled Searching of Game Trees
Vrba, Jan ; Hric, Jan (advisor) ; Majerech, Vladan (referee)
Title: A Controlled Searching of Game Trees Author: Jan Vrba Department: Department of Theoretical Computer Science and Mathematical Logic Supervisor: RNDr. Jan Hric Abstract: Monte-Carlo Tree Search is a search algorithm based on random Monte- Carlo playouts. Since it was first introduced in 2006, it has been successfully used in several areas. Most notably for the game Go. MCTS is intended mainly for problems with too large a state space to be fully explored in reasonable time. Working with a large state space and the fact that when evaluating a node, it first explores all possible moves leads to large memory complexity. This work explores options a user can use to regulate memory complexity based on the results of previous Monte-Carlo playouts. Keywords: MCTS, UCT, BMCTS, RAVE
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Genetic Algorithms driven by MCTS
Havránek, Štěpán ; Hric, Jan (advisor) ; Moudřík, Josef (referee)
Evolutionary and genetic algorithms are problem-solving methods designed according to a nature inspiration. They are used for solving hard problems that we cannot solve by any efficient specialized algorithm. The Monte Carlo method and its derivation the Monte Carlo Tree Search (MCTS) are based on sampling and are also commonly used for too complex problems, where we are dealing with enormous memory consumption and it is impossible to perform a complete searching. The goal of this thesis is to design a general problem solving method that is built from these two completely different approaches. We explain and implement the new method on one example problem: the Traveling salesman problem (TSP). Second part of this thesis contains various tests and experiments. We compare different settings and parametrizations of our method. The best performing variant is then compared with the classical evolutionary TSP solution or, for example, with greedy algorithms. Our method shows competitive results. The best results were achieved with the cooperation of our method and the classical evolutionary TSP solution. This union shows better results than any of its parts separately, which we find as a great success.
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