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Artificial Intelligence in Chemistry - B445010

Title:	Umělá inteligence v chemii
Guaranteed by:	Department of Computing and Control Engineering (445)
Faculty:	Faculty of Chemical Engineering
Actual:	from 2019 to 2020
Semester:	summer
Points:	summer s.:5
E-Credits:	summer s.:5
Examination process:	summer s.:
Hours per week, examination:	summer s.:2/2, C+Ex [HT]
Capacity:	unknown / unknown (unknown)
Min. number of students:	unlimited
State of the course:	not taught
Language:	Czech
Teaching methods:	full-time
Level:
Note:	course can be enrolled in outside the study plan enabled for web enrollment

Guarantor:	Hrnčiřík Pavel doc. Ing. Ph.D.
Interchangeability :	N445030

Examination dates Schedule

Annotation -

The course provides basic overview of artificial intelligence covering number of different areas from problem solving by searching in state space to fuzzy logic. In the practical part of the course the emphasis is mainly on logical programming in Prolog and the design of simple fuzzy logic systems in Matlab.

Last update: Fialová Jana (15.01.2018)

Course completion requirements - Czech

Během semestru jsou zadávány 2 samostatné projekty, z kterých je pro získání zápočtu nutné získat alespoň 50 % z max. možného bodového ohodnocení. Vlastní zkouška má písemnou formu.

Last update: Hrnčiřík Pavel (05.02.2018)

Literature -

D: Russell S.,Norvig P.,Artificial Intelligence: A Modern Approach,Prentice Hall, Englewood Cliffs,2002,0137903952

D: Ross J.T., Fuzzy Logic with Engineering Applications, Wiley-Blackwell, 2010, ISBN 978-0470743768

D: Buckley J.J., Eslami E., An Introduction to Fuzzy Logic and Fuzzy Sets (Advances in Intelligent and Soft Computing), Physica, 2008, ISBN 978-3790814477

D: Zadeh L.A., Fuzzy Sets*, Information and Control 8, 338-353 (1965), http://www-bisc.cs.berkeley.edu/Zadeh-1965.pdf

Last update: Hrnčiřík Pavel (05.02.2018)

Requirements to the exam - Czech

Last update: Hrnčiřík Pavel (05.02.2018)

Syllabus -

1. Artificial Intelligence - definition, clasification, basic concepts

2. Problem solving - uninformed strategies

3. Problem solving - informed strategies, heuristic search

4. Two person games: min-max method, alpha-beta pruning

5. Knowledge representation - production systems

6. Knowledge representation - semantic networks, frames

7. Knowledge representation - first-order logic

8. Fuzzy systems, fuzzy sets

9. Fuzzy relation, model of semantic

10. Linguistic operators

11. Inference rules

12. Fuzzy controller

13. Prolog

14. Prolog

Last update: Fialová Jana (15.01.2018)

Learning resources -

Other study aids:

Artificial Intelligence: A Modern Approach http://aima.cs.berkeley.edu/

Edward Sazonov: Fuzzy Logic and Applications, course EE509, Clarkson University, Potsdam, NY. http://www.intelligent-systems.info/classes/ee509/

SWI-Prolog's home http://www.swi-prolog.org/

Interactive MATLAB & Simulink Based Tutorials, http://www.mathworks.com/academia/student_center/tutorials/index.html?s_tid=acmain_lrn_tut

Last update: Fialová Jana (15.01.2018)

Learning outcomes -

Students will be able to:

Know the basics of artificial intelligence.

Write simple programs in Prolog.

Decide whether a given technical problem can be solved using fuzzy logic control.

Design a simple fuzzy logic controller for this problem.

Last update: Fialová Jana (15.01.2018)

Registration requirements -

Matematika I, Algoritmizace a programování

Last update: Fialová Jana (15.01.2018)

Teaching methods
	Activity	Credits	Hours
	Účast na přednáškách	1	28
	Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi	1	28
	Práce na individuálním projektu	1	28
	Příprava na zkoušku a její absolvování	1	28
	Účast na seminářích	1	28
		5 / 5	140 / 140

Coursework assessment
Form	Significance
Regular attendance	20
Report from individual projects	30
Examination test	50