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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2022/2023

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), Ladislava Smítková Janků
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. Introduction to knowledge-based systems.

2. Knowledge-based system architecture, knowledge representation.

3. Inference mechanism, methods for realization of inference mechanism.

4. Expressing and processing uncertainty.

5. Creation of knowledge-based system, ontology, knowledge acquisition.

6. Bayesian networks (example of a calculation).

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

8. Rule inference fuzzy system.

9. Knowledge representation using decision trees.

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

11. Extraction of rules from decision trees.

12. Extraction of rules from neural networks.

13. Application of rules in multiagent systems.

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 2022/2023:
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
roomTH:A-1142
Smítková Janků L.
09:15–10:45
(lecture parallel1
parallel nr.101)

Thákurova 7 (FSv-budova A)
Apple lab
roomTH:A-1142
Smítková Janků L.
11:00–12:30
(lecture parallel1
parallel nr.102)

Thákurova 7 (FSv-budova A)
Apple lab
Tue
Wed
Thu
roomT9:107
Jiřina M.
16:15–17:45
(lecture parallel1)
Dejvice
Posluchárna
Fri
Time-table for summer semester 2022/2023:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2022-11-28
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet3463706.html