Knowledge-based Systems

The course is not on the list Without time-table
Code Completion Credits Range Language
BI-ZNS.21 Z,ZK 5 2P+2C Czech
Garant předmětu:
Department of Applied Mathematics

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.


Entry knowledge: 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. Rout J. K., Rout M., Das H. : Machine Learning for Intelligent Decision Science (Algorithms for Intelligent Systems). Springer, 2020. ISBN 978-981-15-3689-2.

2. Kendal S., Creen M. : An Introduction to Knowledge Engineering. Springer, 2006. ISBN 978-1846284755.

3. Brachman R., Levesque H. : Knowledge Representation and Reasoning. Morgan Kaufmann, 2004. ISBN 978-1558609327.

4. Akerkar R., Sajja P. : Knowledge-Based Systems. Jones & Bartlett Learning, 2009. ISBN 978-0763776473.

Further information:
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2023-06-07
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