Srovnání paradigmat strojového učení
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Vysoká škola báňská – Technická univerzita Ostrava
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Abstract
The aim of this thesis is to summarize the theory of machine learning in multi-agent systems, their
basic paradigms and compare the basic features and functionalities of selected machine learning
methods through implementation. The first part of the thesis deals with the introduction of machine
learning and the description of each paradigm. The second part devoted to a case study.
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machine learning, agent, multiagent system, symbol-based, genetic algorithms, stochastic, connectionist
approaches