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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
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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3D strategic-combat multiplayer computer game
Stacho, Jakub ; Ježek, Pavel (advisor) ; Gemrot, Jakub (referee)
Many players like to play strategic and RPG games. Nowadays, is almost necessary, that player can play against players all over the world, which will be the key element in designing and implementation of our game. The goal of the thesis is to design and im- plement a 3D computer game with multiplayer mode with action and strategic elements, which is played from a third-person view while using modern tools for creating games. The player can gather wood from trees in the world, which can be used to either build or upgrade structures and creating nonplayer entities, that are gathering this resource by themselves. In the game, we can found neutral buildings. When they are captured, they produce other types of resources, which can be used for equipment to be able to fight against other players. The goal of the game is to destroy the enemy's main building. The game offers multiplayer mode 1v1, where any player around the world can connect and play. The game is implemented using the Unity game engine, which offers wide options for game creation. In this thesis, we are solving matchmaking problems and implementing our own solution, which offers players to either create or connect to a game. Within pro- blem analysis, we discuss choosing the best framework supporting creating multiplayer games inside Unity, or we...
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Multiagent Support for Strategic Games
Knapek, Petr ; Uhlíř, Václav (referee) ; Zbořil, František (advisor)
This thesis is dedicated to creating a new system with capabilities to create new generic, autonomous strategy computer game controlling elements based on multi-agent systems with social, intelligent decision-making and learning skills. Basic types of strategy games and problems of their playing will be introduced, along with currently used methods of intelligent game AI development. This thesis also presents design and implementation of the new system, working model for a specific game and results obtained while testing it.
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Strategic Game Based on Multiagent Systems
Knapek, Petr ; Kočí, Radek (referee) ; Zbořil, František (advisor)
This thesis is focused on designing and implementing system, that adds learning and planning capabilities to agents designed for playing real-time strategy games like StarCraft. It will explain problems of controlling game entities and bots by computer and introduce some often used solutions. Based on analysis, a new system has been designed and implemented. It uses multi-agent systems to control the game, utilizes machine learning methods and is capable of overcoming oponents and adapting to new challenges.
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Generic Realtime Strategies
Baláž, Tibor ; Dvořák, Filip (advisor) ; Černý, Martin (referee)
Strategy games are a field of digital entertainment that has always attracted large audiences of players and researchers - be it for modeling military strategy, economical principles or automated decision making and artificial intelligence. The thesis is focused on developing an environment that allows to efficiently prototype strategic games with a high level of abstraction. The theoretical part of the thesis defines what is a strategy game, gives introduction into the game engines and describes a new language used for defining the games. The practical part describes how is the language interpreted into the game engine and how the execution of the game proceeds. Powered by TCPDF (www.tcpdf.org)
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Generic Realtime Strategies
Baláž, Tibor ; Dvořák, Filip (advisor) ; Černý, Martin (referee)
Strategy games are a field of digital entertainment that has always attracted large audiences of players and researchers - be it for modeling military strategy, economical principles or automated decision making and artificial intelligence. The thesis is focused on developing an environment that allows to efficiently prototype strategic games with a high level of abstraction. The theoretical part of the thesis defines what is a strategy game, gives introduction into the game engines and describes a new language used for defining the games. The practical part describes how is the language interpreted into the game engine and how the execution of the game proceeds. Powered by TCPDF (www.tcpdf.org)
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Influence of games mechanisms on user experience
Kracík, Radim ; Brejcha, Jan (advisor) ; Šisler, Vít (referee)
The work deals with the relationship between game mechanics and user experience. Identifies the basic gameplay mechanics and elements that have the most important influence on the quality of user experience. Research is realized on the example of a specific game using the concept of game design patterns. Part of the work deals with the introduction of this concept. Furthermore, the work deals with the general theories of games, models of classification and history of computer games. Part of the work is a description of the author's practical experience in creating games as a school project.
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Artificial Intelligence in Strategic Computer Games
Votroubek, Lukáš ; Přibyl, Bronislav (referee) ; Zuzaňák, Jiří (advisor)
This work covers with artificial intelligence of strategy computer games, however many of these methods are usable in other areas. These are different methods used in deciding (finite state machina, fuzzy logic, Markov Process), planning (Goal-oriented action planning, Montecarlo planning, Case-based planning) and machine learning ( Reinforcement leasing, Decision Learning and Neural Networks). Objective of this thesis is to study this methods from different sources and explain their base principle. Then few of this methods resolve in more details and implement them (goal oriented planning and state machine). This thesis focuses on game engine ORTS, which is used in implementing and testing methods.
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