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The course covers selected areas of artificial intelligence focusing primarily on different approaches to knowledge representation and reasoning both under certainty and uncertainty. In the practical part of the course the emphasis is mainly on rule-based programming in CLIPS and the design of fuzzy logic systems in Matlab.
Last update: Pátková Vlasta (20.04.2018)
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Zápočet: Během semestru jsou na cvičeních zadávány samostatné úlohy, které je nutné vypracovat pro získání zápočtu. Dále je zadáván samostatný projekt, ze kterého je pro získání zápočtu nutné získat alespoň 50 % z max. možného bodového ohodnocení. Zkouška: Zápočet předchází zkoušce, tj. bez získání zápočtu nelze zkoušku absolvovat. Vlastní zkouška má písemnou formu. Last update: Hrnčiřík Pavel (08.02.2024)
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R: Giarratano J.C., Riley G.D.,Expert Systems: Principles and Programming,Course Technology,New York,2004,0534384471 R: Russell S.,Norvig P.,Artificial Intelligence: A Modern Approach,Prentice Hall, Englewood Cliffs,2002,0137903952 R: Ross J.T., Fuzzy Logic with Engineering Applications, Wiley-Blackwell, 2010, ISBN 978-0470743768 R: Passino K.M., Yurkovich S., Fuzzy Control, Addison-Wesley,New York, 1998,020118074X R: Buckley J.J., Eslami E., An Introduction to Fuzzy Logic and Fuzzy Sets (Advances in Intelligent and Soft Computing), Physica, 2008, ISBN 978-3790814477 A: Zadeh L.A., Fuzzy Sets*, Information and Control 8, 338-353 (1965), http://www-bisc.cs.berkeley.edu/Zadeh-1965.pdf Last update: Pátková Vlasta (20.04.2018)
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Písemná zkouška Last update: Mareš Jan (04.10.2023)
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1 Fuzzy logic. Mamdani inference method. Sugeno inference method. 2 Fuzzy controller design 3 Using the Matlab Fuzzy toolbox and Simulink for FC implementation 4 Individual project – fuzzy controller 5 Using Matlab toolboxes for optimization of FC presentation and outputs 6 Adaptive neuro-fuzzy inference system 7 Optimization of a fuzzy controller using machine learning 8 Case study: fuzzy control of a model bioprocess 9 Introduction to knowledge-based control: direct vs. supervisory control strategies 10 Rule-based programming and the CLIPS language - Part 1 11 Rule-based programming and the CLIPS language - Part 2 12 Rule-based programming and the CLIPS language - Part 3 13 Handling uncertainty in knowledge representation and reasoning 14 Inference systems-probabilistic approach Last update: Hrnčiřík Pavel (26.08.2024)
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Other study aids: Edward Sazonov: Fuzzy Logic and Applications, course EE509, Clarkson University, Potsdam, NY. http://www.intelligent-systems.info/classes/ee509/ Gary Riley: A Tool for Building Expert Systems. http://clipsrules.sourceforge.net/ Interactive MATLAB & Simulink Based Tutorials, http://www.mathworks.com/academia/student_center/tutorials/index.html?s_tid=acmain_lrn_tut Last update: Pátková Vlasta (20.04.2018)
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Students will be able to: Decide whether a given technical problem can be solved using fuzzy logic control. Design an appropriate fuzzy logic controller for this problem. Propose optimization of its structure. Select an appropriate knowledge representation paradigm for a given technical problem. Design and implement rule-based systems in CLIPS. Last update: Pátková Vlasta (20.04.2018)
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| Teaching methods | ||||
| Activity | Credits | Hours | ||
| Účast na přednáškách | 1 | 28 | ||
| Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi | 0.7 | 20 | ||
| Práce na individuálním projektu | 0.8 | 22 | ||
| Příprava na zkoušku a její absolvování | 1 | 28 | ||
| Účast na seminářích | 0.5 | 14 | ||
| 4 / 4 | 112 / 112 | |||
| Coursework assessment | |
| Form | Significance |
| Regular attendance | 20 |
| Report from individual projects | 30 |
| Examination test | 50 |