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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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Coalition Games in a Dynamic Multiagent Environment
Hamran, Peter ; Uhlíř, Václav (referee) ; Zbořil, František (advisor)
The essence of this bachelor thesis was to implement an algorithm for suitable coalition forming in multi-agent system. Suitable coalition denotes a coalition which is not formed in super-additive environment and so it depends on its size and members. Aim of this thesis was to adjust an algorithm for coalition bounds forming between agents in a way, that it is applicable in a multi-agent competition MASSIM. Key aspects are amount of messages sent between agents for coalition to form and also a time it takes to calculate all coalitional values required for such a process. Final scenario is where two competitive groups of agents compete for resources in a multi-agent system simulation.
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Multi-Agent Strategy Game over Ants
Šimetka, Vojtěch ; Zbořil, František (referee) ; Král, Jiří (advisor)
This thesis describes challenges in design and development of a multi-agent real-time strategy game. It discusses necessary theoretical background and its consequences for the game design. The resulting game implements, apart from features and game control which can be found in nowadays RTS games, three different levels of artificial intelligence in which each unit is an agent. Moreover, there is a unique cooperation mode, where units can be controlled by user and artificial intelligence at the same time. In addition, the game is designed in such way that it can be easily extended with new artificial intelligences, therefore, used for teaching agent systems. A number of experiments was performed in order to evaluate both game design and capabilities of artificial intelligence.
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Hyperparameter optimization in AutoML systems
Pešková, Klára ; Neruda, Roman (advisor) ; Awad, Mariette (referee) ; Kordik, Pavel (referee)
In the last few years, as processing the data became a part of everyday life in different areas of human activity, the automated machine learning systems that are designed to help with the process of data mining, are on the rise. Various metalearning techniques, including recommendation of the right method to use, or the sequence of steps to take, and to find its optimum hyperparameters configuration, are integrated into these systems to help the researchers with the machine learning tasks. In this thesis, we proposed metalearning algorithms and techniques for hyperparameters optimization, narrowing the intervals of hyperparameters, and recommendations of a machine learning method for a never before seen dataset. We designed two AutoML machine learning systems, where these metalearning techniques are implemented. The extensive set of experiments was proposed to evaluate these algorithms, and the results are presented.
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Smart Traffic Intersection
Škopková, Věra ; Barták, Roman (advisor) ; Forst, Libor (referee)
This thesis is concerned with the problem of planning paths for autonomous cars through a smart traffic intersection. In this thesis, we describe existing concepts for solving this problem and discuss the possibilities of approaching intersection problems theoretically. Then, we choose one specific approach and design a declarative model for solving the problem. We use that model to perform a series of theoretical experiments to test the throughput and the quality of intersection paths described by different graphs. After that, we translate theoretical plans to actions for real robots and run it. In these experiments, we measure the degree of robots desynchronization and performance success of the plans based on the collision rate. We also describe how to improve action translation to achieve better performance than that for real robots following the straightforward plans.
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Coalition Games in a Dynamic Multiagent Environment
Hamran, Peter ; Uhlíř, Václav (referee) ; Zbořil, František (advisor)
The essence of this bachelor thesis was to implement an algorithm for suitable coalition forming in multi-agent system. Suitable coalition denotes a coalition which is not formed in super-additive environment and so it depends on its size and members. Aim of this thesis was to adjust an algorithm for coalition bounds forming between agents in a way, that it is applicable in a multi-agent competition MASSIM. Key aspects are amount of messages sent between agents for coalition to form and also a time it takes to calculate all coalitional values required for such a process. Final scenario is where two competitive groups of agents compete for resources in a multi-agent system simulation.
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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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Adaptive Matchmaking Algorithms for Computational Multi-Agent Systems
Kazík, Ondřej ; Neruda, Roman (advisor) ; Paprzycki, Marcin (referee) ; Diamantini, Claudia (referee)
The multi-agent systems (MAS) has proven their suitability for implementation of complex software systems. In this work, we have analyzed and designed the data mining MAS by means of role-based organizational model. The organiza- tional model and the model of data mining methods have been formalized in the description logic. By matchmaking which is the main subject of our research, we understand the recommendation of computational agents, i.e. agents encap- sulating some computational method, according their capabilities and previous performances. The matchmaking thus consist of two parts: querying the ontol- ogy model and the meta-learning. Three meta-learning scenarios were tested: optimization in the parameter space, multi-objective optimization of data min- ing processes and method recommendation. A set of experiments in these areas have been performed. 1
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Modern ways to design fully distributed, decentralized and stealthy worms
Szetei, Norbert ; Krištofič, Milutín (advisor) ; Balyo, Tomáš (referee)
The thesis deals with the study of the computer worm meeting several criteria (it should be fully distributed, decentralized and stealthy). These conditions lead to anonymity, longevity and better security of our worm. After presenting the recently used architectures and new technologies we analyse the known implementations. We propose the solutions with the new design together with the possible ways of improvements. In the next chapter we study biological concepts suitable for the new replication mode, where we implement the key concepts of functionality in a higher programming language. At design we have considered as important to be platform independent, so it is possible for the worm to spread in almost every computer environment, in dependence of implementation of the required modules. Powered by TCPDF (www.tcpdf.org)
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Distributed Monte-Carlo Tree Search for Games with Team of Cooperative Agents
Filip, Ondřej ; Lisý, Viliam (advisor) ; Majerech, Vladan (referee)
The aim of this work is design, implementation and experimental evaluation of distributed algorithms for planning actions of a team of cooperative autonomous agents. Particular algorithms require different amount of communication. In the work, the related research on Monte-Carlo tree search algorithm, its parallelization and distributability and algorithms for distributed coordination of autonomous agents. Designed algorithms are tested in the environment of the game of Ms Pac-Man. Quality of the algorithms is tested in dependence on computational time, the amount of communication and the robustness against communication failures. Particular algorithms are compared according to these characteristics. Powered by TCPDF (www.tcpdf.org)
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