|
|
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
|
|
|
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.
|
|
|
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.
|
|
|
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.
|
|
|
Chess Program with Various Chess Variations with New Figure
Dostál, Martin ; Křena, Bohuslav (referee) ; Rozman, Jaroslav (advisor)
This thesis deals with an analysis and evaluation of methods and algorithms needed for successfull implementation of a chess program. Both the basic principles and further currently used extensions of these methods and algorithms are stated with a goal to implement a quality chess program playing different chess variations including a new figure. This thesis also introduces the basic pillars of every chess program - these pillars include board representation, state space search, evaluation of a game state and changes required for implementation of new chess variations. A comparison of difficulties of individual methods is included for an overview.
|
|
|
Mobil Application Support for Nine Men's Morris Playing
Kolínek, Daniel ; Zbořil, František (referee) ; Zbořil, František (advisor)
The aim of this work is to create an application for mobile device solving the task of determination the best move in game Nine men's morris from a camera snapshot taken on smart device. The task is divided into the problem of position detection and determination of the best move. Position recognition is solved by using edge detection, finding circles using Hough transform and subsequent color detection in found circles. Finding the best move is solved by own position evaluation and state space search using the Alpha-Beta algorithm. Using the OpenCV library and the Android Studio development environment, a sample application executable under Android version 5 and higher was created. The sample application solves both tasks.
|
|
|
Chess Program for Bughouse Variant
Staňa, Marek ; Křena, Bohuslav (referee) ; Rozman, Jaroslav (advisor)
This thesis describes process of creating a chess program playing Bughouse variant allowing to play against human or other programs. Firstly explains difference in Bughouse rules from classic chess, main part is about artificial intelligence. It compares individual methods used for making chess programs and adapts them to Bughouse variant.
|
|
|
Artificial intelligence for Mariáš
Kaštánková, Petra ; Veselý, Pavel (advisor) ; Pangrác, Ondřej (referee)
This thesis focuses on the implementation of a card game, Mariáš, and an artificial intelligence for this game. The game is designed for three players and it can be played with either other human players, or with a computer adversary. The game is designed as a client-server application, whereby the player connects to the game using a web page. The basis of the artificial intelligence is the Minimax algorithm. To speed it up we use the Alpha-Beta pruning, hash tables for storing equivalent states of the game and various heuristics. Powered by TCPDF (www.tcpdf.org)
|
|
|
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.
|
|
| |
guest ::
