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TiledPlanet: game map generator
Konderla, Bartosz ; Starka, Tomáš (referee) ; Vlnas, Michal (advisor)
Práce je zaměřená na procedurální generování sférických map pro tahové strategické hry inspirované herní sérií Civilization. Proces generování byl implementován v herním enginu Unity s použitím různých technik, jako jsou sférické mřížky, šum, Bézierovy křivky, úroveň detailů a výpočetní shadery. Práce také zkoumá mechaniky tahových strategických her související s mapou, jako jsou hledání cest a mlha války. Výsledkem práce je aplikace schopná generovat planetárně vyhlížející mapy, doplněná o nástroj umožňující uživatelské editování map a ukázku základních herních mechanik. Implementace může sloužit jako základ pro vývoj tahových strategických her.
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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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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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Multiagent Support for Strategic Games
Válek, Lukáš ; Kočí, Radek (referee) ; Zbořil, František (advisor)
This thesis is focused on design of framework for creation an articial opponents in strategy games. We will analyze different types of strategy games and artificial intelligence systems used in these types of games. Next we will describe problems, which can occur in these systems and why agent-based systems makes better artificial opponents. Next we will use knowledge from this research to design and implement framework, which will act as support for creating an artificial intelligence in strategy games.
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Strategy for Game Systems
Švestka, Marek ; Tóth, Michal (referee) ; Zemčík, Pavel (advisor)
This thesis resolves the artificial intelligence representation in computer games. The goal was to find it's suitable interpretation methods. The issue was solved with "map"implementation, which is the n-dimension matrix holding the information about current game state for the concrete player. There were various types of these matrixes used in this thesis. Furthermore was designed a way to evolve computer opponents during the gameplay and created an algorithm, that gives the idea of the space in which it is located. Benefit from this thesis is a solution that makes artificial adversary close to human gameplay behavior. The outcome could also be used in simulators for military battles in real world.
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Interconnection of Recent Strategic Games with Multi-Agent Frameworks
Válek, Lukáš ; Kočí, Radek (referee) ; Zbořil, František (advisor)
This thesis is focused on design of framework for creation an articial opponents in strategy games. We will analyze different types of strategy games and artificial intelligence systems used in these types of games. Next we will describe problems, which can occur in these systems and why agent-based systems makes better artificial opponents. Next we will use knowledge from this research to design and implement framework, which will act as support for creating an artificial intelligence in strategy games.
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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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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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