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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2019/2020

Knowledge-based Systems

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Code Completion Credits Range Language
BI-ZNS Z,ZK 5 2P+2C Czech
Lecturer:
Marcel Jiřina (guarantor)
Tutor:
Marcel Jiřina (guarantor), Klára Hájková
Supervisor:
Department of Applied Mathematics
Synopsis:

Students will become familiar with the systems based on knowledge (knowledge-based systems), which are systems that usetechniques of artificial intelligence to solve problems that require human judgment, learning and reasoning from findingsand actions. The course introduces students to the philosophy and architecture of knowledge-based systems to support decision-makingand planning. The course assumes knowledge of set theory, probability theory, artificial neural networks, and evolutionary algorithms.

Requirements:

Basic knowledge of mathematical logic, probability and statistics.

Syllabus of lectures:

1. Syllabus of lectures and seminars, conditions for assessment and completion of the course, introduction to knowledge systems

2. Knowledge system architecture, knowledge representation

3. Inference mechanism, methods for realization of inference mechanism

4. Expressing and processing uncertainty

5. Creation of knowledge system, ontology, knowledge acquisition

6. Bayesian networks (example of calculation)

7. Multivalued logic, fuzzy logic, operations in fuzzy logic

8. Rule inference fuzzy system

9. Knowledge representation by means of decision trees

10. Neural networks and their use for knowledge representation and rule inference

11. Extraction of rules from decision trees and neural networks

12. Multiagent systems

13. Reserve

Syllabus of tutorials:

1. Introductory exercise, familiarization with evaluation rules and the framework for tasks.

2. Knowledge representation. Assignment and work on the 1st task.

3. Submission of the 1st task.

4. Inference and explanatory mechanism. Assignment and work on the 2nd task.

5. Submission of the 2nd task.

6. Uncertainty. Assignment and work on the 3rd task.

7. Submission of the 3rd task.

8. Fuzzy logic. Assignment and work on the 4th task.

9. Extraction of rules 1

10. Submission of the 4th task.

11. Neural networks

12. Extraction of rules 2

13. Submission of the final task and granting credits.

Study Objective:
Study materials:

[1] Akerkar, R. - Sajja, P.: Knowledge-Based Systems, Jones &; Bartlett Learning, 2009, 0763776475,

[2] Kendal, S. - Creen, M.: An Introduction to Knowledge Engineering, Springer, 2006, 1846284759,

[3] Brachman, R. - Levesque, H.: Knowledge Representation and Reasoning, Morgan Kaufmann, 2004, 1558609326,

Note:
Further information:
https://courses.fit.cvut.cz/BI-ZNS/
Time-table for winter semester 2019/2020:
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue
Fri
Thu
Fri
roomT9:107
Jiřina M.
09:15–10:45
(lecture parallel1)
Dejvice
Posluchárna
roomT9:303
Hájková K.
12:45–14:15
(lecture parallel1
parallel nr.2)

Dejvice
NBFIT PC ucebna
roomT9:303
Hájková K.
14:30–16:00
(lecture parallel1
parallel nr.3)

Dejvice
NBFIT PC ucebna
roomT9:303
Hájková K.
16:15–17:45
(lecture parallel1
parallel nr.4)

Dejvice
NBFIT PC ucebna
roomT9:303
Hájková K.
11:00–12:30
(lecture parallel1
parallel nr.1)

Dejvice
NBFIT PC ucebna
Time-table for summer semester 2019/2020:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2019-09-21
For updated information see http://bilakniha.cvut.cz/en/predmet3463706.html