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Behaviour-based control of autonomous robots
Gažo, Matúš ; Obdržálek, David (referee) ; Mráz, František (advisor)
The aim of this bachelor's thesis was an implementation of a library, which should enable to develop controllers for autonomous mobile robots. The controllers are based on ideas of Behaviour-based robotics. This means that such a robot has a set of elementary behaviours and its control system consists of a set of parametrized rules, which decide according to the state of its sensors which one of the pre-defined elementary behaviours the robot has to use. This program shifts the substantial part of a robot control development from programming to defining such elementary behaviours and sets of rules. It includes a genetic algorithm that can help to optimize parameters of the rules used and/or the behaviours used in a controller. As a proof-of-concept a simulated experiment with multiple agents was conducted, in which the robots were assigned a simple construction task. All simulations were done in the Player/Stage simulator.
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Machine learning of formal languages
Klonfar, Matěj ; Bednárek, David (referee) ; Mráz, František (advisor)
In the present work I study the task of machine learning of formal languages. The task of the work is to design and implement the program with anyone will be able to study the progress and results of algorithms for learning languages. With this program is possible to run algorithms for learning from examples. Examples could be sets of positive and negative examples of the result languages or sets expressed by teacher who knows the result language. The main task is to design easily extensible application with a view to inserting add-ins without no limits of used representation of learned languages or language complexity.
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Development of robotic soccer players by methods of evolutionary programming
Dener, Libor ; Petříčková, Zuzana (referee) ; Mráz, František (advisor)
In the present work we study and implmement means to evolve players of robotic soccer. We implement our own simplied model of soccer play and libraries for genetic algorithms and artical neural networks. We focus on players controlled by artical neural networks in the simplied model. We use incremental learning approach in which we rst train the players on simpler subtasks and then we use these players on more complex problems. The neural networks are evolved by genetic algorithms. We demonstrate dierent variants of genetic algorithms and we discuss achieved results.
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Compiler generator based on restarting automata
Procházka, Jan ; Plátek, Martin (referee) ; Mráz, František (advisor)
Restarting automata, in their most general form, represent a very strong theoretical model recognizing much wider class of languages than the class of context-free ones. Hence, our goal was to design a tool which for a given restarting automaton (in human-readable format) generates a program computing the meaning of an input text. In order to enable that, this thesis extends the model of restarting automata by adding semantics to its meta-instruction. The resulting program is a compiler-compiler (CCRA) inspired by the tools such as flex or bison. However, the CCRA uses restarting automaton instead of a context-free grammar. The implementation as well as the output are realized in C++ which ensures the compatibility with both Windows and Linux systems.
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Parallel evolutionary algorithms for multiobjective optimization
Pilát, Martin ; Mráz, František (referee) ; Neruda, Roman (advisor)
In the present work we study the options for parallelization of evolutionary algorithms for multiobjective optimization (MOGA). We provide the overview of existing sequential and parallel MOGAs and we propose three other methods: FCMOGA - MOGA with fuzzy constraints, HIMOGA - heterogeneous island MOGA, and MOGASOLS - MOGA with single objective local search. We test these algorithms on a set of benchmark problems and compare them with existing MOGAs.
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Using domain knowledge in applications of GUHA procedures
Ralbovský, Martin ; Mráz, František (referee) ; Rauch, Jan (advisor)
We presume for this work, that the GUHA procedures' abilities can be extended by using domain knowledge in a suitable form. There are two types of domain knowledge used in this work: background knowledge and ontologies. Background knowledge is a relatively unknown, yet perspective type of domain knowledge containing rules from domain experts in a written form. The work describes this knowledge and introduces suitable rule formalization. Furthermore, a tool for rule validation is described and implemented. The implementation is done in the Ferda environment, a modular environment for visual GUHA data mining. The work also describes first practical experience with this tool. Concerning domain ontologies, the work greatly extends up-to-date knowledge of usage of ontologies in the GUHA data mining. There are all the former techniques for ontology usage mentioned and extended; in suitable cases the implementation design in Ferda environment is presented. The results of the work are discussed with respect to further usage in the Ever-Miner system, a next generation knowledge discovery tool
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Modeling of plants using "Point-sprites"
Klecanda, Václav ; Pelikán, Josef (advisor) ; Mráz, František (referee)
In the present work i study possibilities of using elemets of hardware accelerated computer graphics called Point sprites in modeling and rendering of natural objects as trees, bushes, flowers. Next goal of the work is possibility of connecting using Point sprites with one of commonly used technique LoD( Level of Detail) for achieving best effectivity of rendering.
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Using Cellular Automata for Data Encryption
Dvořák, Martin ; Trunda, Otakar (advisor) ; Mráz, František (referee)
Cellular automata are discrete systems with very simple rules but very diverse behaviour. Some cellular automata can generate high-quality pseudorandom bit sequences. This leads us to the question of whether cellular automata could be used in cryptography, as a replacement for stream ciphers for instance. We will create and compare various methods for generating long one-time-pads from short keys, where our methods will utilize cellular automata. Besides direct design of cryptographical algorithms, we will also create an evolutionary algorithm, which will try to connect our building blocks in the best possible way. The outcome of our work will be a Windows desktop application for file encryption. Powered by TCPDF (www.tcpdf.org)
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