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Opening Planner for Computer Game StarCraft: Brood War
Dostál, František ; Gemrot, Jakub (advisor) ; Hric, Jan (referee)
Starcraft: Broodwar is popular strategic game and it's gameplay, automated or not, provides many issues to solve. One of these problems is the choice of early-game buildorder in a situation there is no access to any information about players opponents or their strategies. Most playing agents solves this problem by random choicefrom standardised strategies. These strategies have to be preplanned. Goal of this theses is then to implement offline planner capable of preplanning concrete steps so that if the agent follows them precisely, the plan will be completed in optimal time. The planning is done via search through temporal state space. 1
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Artificial Intelligence for the Game Fantasy Realms
Miklóšová, Tereza ; Hric, Jan (advisor) ; Zelinka, Mikuláš (referee)
In this thesis we work on the implementation of the card game Fantasy Realms for simultaneous play of more than one player and the artificial intelligence capable of playing this game. The artificial intelligence is based on greedy algorithm which we reworked to greedy algorithm with greater insight and on reinforcement learning. Greedy algorithm has been proved to be a good model for playing this game and has achieved great results in regard to average score. On the other hand learning agent based on reinforcement learning has not been very successful, because the model of the game we provided to it was not satisfactory for learning purposes. Thus the agent learned by reinforcement learning could not overcome the greedy one. 1
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Artificial intelligence for the game of Azul
Počatko, Michal ; Dingle, Adam (advisor) ; Hric, Jan (referee)
A comparison between three different approaches to developing an AI agent for the board game Azul and their implementation, testing and consequent results of said tests. A part of the thesis is also a simulator created in a game engine for playing against a local player or an artificial intelligence agent.
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Effective Algorithms for Verifying Goals in Computer Games
Suda, Martin ; Hric, Jan (advisor)
In the present work we study non-uniform methods for searching game trees of two player games with perfect information. Particularly the non-uniformity based on threats as realized in lambda search and dual-lambda search algorithms is investigated. Threats, de ned as such attack moves that if followed by a pass from the defender result in his certain loss, allow for a reduction of the search space while guaranteeing correctness at the same time. The work then describes a new method for construction of so called relevancy zones, a list of moves or places on the game desk that can only have in uence on the result of the problem in question. Using these zones it is possible to speed up the mentioned algorithms considerably. In the work there are also described three games, AtariGo, Hex and Go-Moku, and their appropriateness with respect to the studied methods is analyzed. Part of the work is also an implementation of the methods for these games using known techniques (transposition tables, history heuristic).
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Artificial Inteligence for Draughts
Bělíček, David ; Švancara, Jiří (advisor) ; Hric, Jan (referee)
Draughts is a board game that is played all around the world in various forms. The aim of this thesis is to describe and implement an artificial intelligence algorithm that will be able to play draughts. We will explain the working of Minimax algorithm, how to enhance it using Alpha-Beta pruning, and its limited-depth version, which uses heuristic evaluations. We will present two hand-crafted heuristic evaluations, how such heuristic evaluation can be replaced with a neural network, and how to develop these networks using evolutionary algorithms. Finally, we will perform experiments in which we will test the created heuristics and networks. At the end of the thesis, we present a tournament that decides which of the developed algorithms is the best.
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Artificial intelligence for the Game Carcassonne: The Discovery
Motlíček, Ondřej ; Hric, Jan (advisor) ; Zelinka, Mikuláš (referee)
The bachelor paper deals with the development of an artificial intelligence for the game Carcassonne - The Discovery. Different approaches for designing an artificial intel- ligence are presented. Heuristic functions based on various aspects of the game. Monte Carlo methods and the Expectiminimax algorithm are used for state space of the game. The designed methods are implemented and experimentally tested and compared by simulations of the game between the artificial players. Results of the experiment are pre- sented and explained. The simulation environment consists of multiple programs for the game simulation of both artificial and human players. A batch simulation of the artificial intelligence is emphasized. 1
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Settlers of Catan
Novák, Daniel ; Hric, Jan (advisor) ; Pilát, Martin (referee)
In this thesis, we work on implementation of the board game Settlers of Ca- tan and artifitial intelligence playing this game. The artificial intelligence is based on a combination of expectimax and reinforcement learning. Using reinforcement learning, we have been able to develop an agent who can play reasonably. We ma- naged to improve the policy learned by reinforcement learning using expectimax. The resulting agent is able to win aganist average human player.
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Board game with artificial intelligence
Crha, Daniel ; Pilát, Martin (advisor) ; Hric, Jan (referee)
Multiplayer board games with imperfect information present a difficult challenge for many common game-playing algorithms. Studying their behavior in such games can be difficult, because existing implementations of such games have poor support for artificial intelligence. This thesis aims to implement an imperfect information multiplayer board game in a way that provides a framework for developing and testing different types of artificial intelligence for board games with the aforementioned qualities. Furthermore, this thesis explores the implementation of several algorithms for the game. This aims to showcase the artificial intelligence framework, as well as to analyze the performance of ex- isting algorithms when applied to a board game with elements such as hidden information and multiple players. 1
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GPU-accelerated Mahalanobis-average hierarchical clustering
Šmelko, Adam ; Kratochvíl, Miroslav (advisor) ; Hric, Jan (referee)
Hierarchical clustering algorithms are common tools for simplifying, exploring and analyzing datasets in many areas of research. For flow cytometry, a specific variant of agglomerative clustering has been proposed, that uses cluster linkage based on Mahalanobis distance to produce results better suited for the domain. Applicability of this clustering algorithm is currently limited by its relatively high computational complexity, which does not allow it to scale to common cytometry datasets. This thesis describes a specialized, GPU-accelerated version of the Mahalanobis-average linked hierarchical clustering, which improves the algorithm performance by several orders of magnitude, thus allowing it to scale to much larger datasets. The thesis provides an overview of current hierarchical clustering algorithms, and details the construction of the variant used on GPU. The result is benchmarked on publicly available high-dimensional data from mass cytometry.
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