Knowledge-based Systems

Course guarantor:

Lecturer:

Tutor:

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/

No time-table has been prepared for this course

The course is a part of the following study plans:

• Bachelor program Informatics, unspecified branch, in Czech, 2015-2020 (VO)
• Bachelor branch Security and Information Technology, in Czech, 2015-2020 (elective course)
• Bachelor branch Computer Science, in Czech, 2015-2020 (elective course)
• Bachelor branch Computer Engineering, in Czech, 2015-2020 (elective course)
• Bachelor branch Information Systems and Management, in Czech, 2015-2020 (compulsory course of the specialization)
• Bachelor branch Web and Software Engineering, spec. Software Engineering, in Czech, 2015-2020 (elective course)
• Bachelor branch Web and Software Engineering, spec. Web Engineering, in Czech, 2015-2020 (elective course)
• Bachelor branch Web and Software Engineering, spec. Computer Graphics, in Czech, 2015-2020 (elective course)
• Bachelor branch Knowledge Engineering, in Czech, 2018-2020 (compulsory course of the specialization)