Knowledgebased Systems
Code  Completion  Credits  Range  Language 

BIZNS.21  Z,ZK  5  2P+2C  Czech 
 Garant předmětu:
 Marcel Jiřina
 Lecturer:
 Marcel Jiřina
 Tutor:
 Ladislava Smítková Janků
 Supervisor:
 Department of Applied Mathematics
 Synopsis:

Students will become familiar with the systems based on knowledge (knowledgebased 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 knowledgebased systems to support decisionmakingand planning. The course assumes knowledge of set theory, probability theory, artificial neural networks, and evolutionary algorithms.
 Requirements:

Entry knowledge: Basic knowledge of mathematical logic, probability and statistics.
 Syllabus of lectures:

1. Introduction to knowledgebased systems.
2. Knowledgebased system architecture, knowledge representation.
3. Inference mechanism, methods for realization of inference mechanism.
4. Expressing and processing uncertainty.
5. Creation of knowledgebased 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 9789811536892.
2. Kendal S., Creen M. : An Introduction to Knowledge Engineering. Springer, 2006. ISBN 9781846284755.
3. Brachman R., Levesque H. : Knowledge Representation and Reasoning. Morgan Kaufmann, 2004. ISBN 9781558609327.
4. Akerkar R., Sajja P. : KnowledgeBased Systems. Jones & Bartlett Learning, 2009. ISBN 9780763776473.
 Note:
 Further information:
 https://courses.fit.cvut.cz/BIZNS/
 Timetable for winter semester 2023/2024:

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 Wed Thu Fri  Timetable for summer semester 2023/2024:
 Timetable is not available yet
 The course is a part of the following study plans:

 Bachelor program, unspecified specialization, in Czech, 2021 (VO)
 Bachelor specialization Artificial Intelligence, in Czech, 2021 (compulsory elective course)
 Bachelor program, unspecified specialization, in Czech, 2024 (VO)
 Bachelor specialization Artificial Intelligence, in Czech, 2024 (compulsory elective course)