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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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GA 19-07635S: Outputs and Results
Rehák, Branislav
This manuscript aims to deliver a survey of results obtained during the solution of the project No. GA19-07635S of the Czech Science Foundation. The timespan dedicated to the work on this project was 1.3.2019 - 30.6.2022. The main area dealt with were\nnonlinear multi-agent systems and their synchronization, further, attention was paid to some auxiliary results in the area of nonlinear observers. This Report briefly introduces the Project, provides a summary of the results obtained and also sketches an outline how these results will be applied and extended in future.
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Virtual Power Plant Anomaly Detection
Vymazal, Jan ; Kočí, Radek (referee) ; Zbořil, František (advisor)
This bachelor thesis deals with the implementation of a multi-agent system for the detection and prediction of anomalies during the operation of a virtual power plant. The thesis also deals with the implementation of a simulation that puts this multi-agent system into an environment that reflects the gradual addition of data in the real world. It also deals with the principles of communication between agents in a multi-agent environment according to FIPA standards. As part of the work, I created the multi-agent system in the JADE framework in the Java programming language and a script in the Python programming language that implements the simulation.
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Agent optimization by means of genetic programming
Šmíd, Jakub ; Neruda, Roman (advisor) ; Kazík, Ondřej (referee)
This thesis deals with a problem of choosing the most suitable agent for a new data mining task not yet seen by the agents. The metric is proposed on the data mining tasks space, and based on this metric similar tasks are identified. This set is advanced as an input to a program evolved by means of genetic programming. The program estimates agents performance on the new task from both the time and error point of view. A JADE agent is implemented which provides an interface allowing other agents to obtain estimation results in real time.
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Multi-Agent systems and organizations
Kúdela, Lukáš ; Štěpánek, Petr (advisor) ; Neruda, Roman (referee)
Multi-agent systems (MAS) are emerging as a promising paradigm for conceptualizing, designing and implementing large-scale heterogeneous software systems. The key advantage of looking at components in such systems as autonomous agents is that as agents they are capable of flexible self-organization, instead of being rigidly organized by the system's architect. However, self-organization is like evolution-it takes a lot of time and the results are not guaranteed. More often than not, the system's architect has an idea about how the agents should organize themselves-what types of organizations they should form. In our work, we tried to solve the problem of modelling organizations and their roles in a MAS, independent of the particular agent platform on which the MAS will eventually run. First and foremost, we have proposed a metamodel for expressing platform-independent organization models. Furthermore, we have implemented the proposed metamodel for the Jade agent platform as a module extending this framework. Finally, we have demonstrated the use of our module by modelling three specific organizations: remote function invocation, arithmetic expression evaluation and sealed-bid auction. Our work shows how to separate the behaviour acquired through a role from the behaviour intrinsic to an agent. This...
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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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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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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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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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