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Main menu for Browse IS/STAG
Course info
KIV / UZI
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Course description
Department/Unit / Abbreviation
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KIV
/
UZI
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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Introduction to Knowledge Engineering
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Form of course completion
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Exam
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Form of course completion
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Exam
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Accredited / Credits
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Yes,
4
Cred.
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Type of completion
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Combined
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Type of completion
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Combined
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Time requirements
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Lecture
2
[Hours/Week]
Tutorial
2
[Hours/Week]
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Course credit prior to examination
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Yes
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Course credit prior to examination
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Yes
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Automatic acceptance of credit before examination
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No
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Included in study average
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YES
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Language of instruction
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-
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Occ/max
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|
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Automatic acceptance of credit before examination
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No
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Summer semester
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0 / -
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0 / -
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0 / -
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Included in study average
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YES
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Winter semester
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0 / -
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7 / -
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0 / -
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Winter semester
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Semester taught
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Winter semester
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Minimum (B + C) students
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10
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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-
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Internship duration
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0
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No. of hours of on-premise lessons |
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Evaluation scale |
1|2|3|4 |
Periodicity |
každý rok
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Evaluation scale for credit before examination |
S|N |
Periodicita upřesnění |
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Fundamental theoretical course |
No
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Fundamental course |
No
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Fundamental theoretical course |
No
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Evaluation scale |
1|2|3|4 |
Evaluation scale for credit before examination |
S|N |
Substituted course
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None
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Preclusive courses
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N/A
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Prerequisite courses
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N/A
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Informally recommended courses
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KIV/UIR
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Courses depending on this Course
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N/A
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Histogram of students' grades over the years:
Graphic PNG
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XLS
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Course objectives:
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The aim of this subject is to learn students to foundations of knowledge systems and knowledge engineering - structures of knowledge systems, knowledge representation, problem solving, inference engine, real knowledge system proposal.
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Requirements on student
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The student has to be able to solve more complex tasks on the computer.
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Content
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1. Basic terms and characteristics of knowledge systems (KS), their application areas. Prerequisites of their development, empty KS (shell)
2. Formal logic and logic programming - formalisms, propositional and first order predicate logic, basic information about Prolog and Python, examples
3. Reasoning principles in propositional and predicate logic, resolution method and its implementation in Prolog or Python, realisation of this method in Prolog/Python
4. Horn clause and programming in Prolog/Python, solving of more complicated tasks in these languages, examples
5. Knowledge representation in knowledge systems, production rules, semantic nets; knowledge retrieval, inference methods, resolution systems, forward and backward chaining, query alternatives, RETE algorithm, non-monotonic reasoning
6. Reasoning uncertainty, hypothetic reasoning a backward induction, sufficiency and necessity rates, probability propagation through inference nets
7. Uncertain derivation, hypothetic derivations and backward induction, rates of sufficiency and necessity, examples
8. Approximate reasoning, belief rates, certainty factors. Dempster-Shafer theory of evidence, fuzzy logic utilization, uncertainty problem solving by fuzzy relations.
9. Knowledge systems architectures, criteria of the best solving determination, conditions of their program realization
10. Centralized and decentralized solutions, knowledge project living cycle and its realiyation in differeni programming languages
11. Creation of several knowledge systems, agent arcitecture of knowledge systems, multiagent systems; methodology of communication with the knowledge systems by different kinds of knowledge models
12. Designing principles and development phases of knowledge and expert systems through knowledge engineer and alternatives of their realisation; demonstration of several realisations
13. Real knowledge systems examples, explanation subsystem, context linkages, types of nodes and rules; real systems implementation and realization
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Activities
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Fields of study
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Stručný úvod do programovacího jazyka Prolog (PDF soubor)
Učebnice Python 3 (PDF soubor)
Literatura a tutorial pro Python (PDF soubor)
Python tutorial (eng) (PDF soubor)
Python tutorial česky (PDF soubor)
Znalostní systémy - kap. 4 skripta Umělá inteligence a rozpoznávání (PDF soubor)
Reprezentace znalostí - Žáček - skriptum Ostravské univerzity (PDF soubor)
Expertní systémy - stručný učební text z VŠE (PDF soubor)
Znalostní systém pro návrh osvětlení - návod (PDF soubor)
Expertní systémy - bakalářská práce z VUT Brno (PDF soubor)
Znalostní technologie - teorie vs. praxe (PDF soubor)
Reprezentace znalostí - skriptum Prof. A. Lukasové, Ostravská univerzita (PDF soubor)
Znalostní systémy - studijní materiály pro rozšířené studium (zip soubor 16 MB)
Dempster-Shaferova teorie - studijní materiály pro domácí studium (zip soubor 1 MB)
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Guarantors and lecturers
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Literature
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Recommended:
Russell, Stuart J.; Norvig, Peter. Artificial intelligence : a modern approach. Upper Saddle River : Prentice Hall, 2003. ISBN 0-13-790395-2.
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Recommended:
Dvořák, J. Expertní systémy. Skriptum VUT Brno, 2004.
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Recommended:
Stefik, M. Introduction to Knowledge Systems. Morgan Kaufman Publ., 1995. ISBN 155860166X.
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Recommended:
Geisler, E. Knowledge and Knowledge Systems. IGI Global Publ, 2007. ISBN 9781599049182.
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Recommended:
Brachman, R.J.; Levesque, H.J. Knowledge Representation. Elsevier, 2004. ISBN 1558609326.
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Recommended:
Abdoullaev, A. Reality, Universal Ontology and Knowledge Systems. IGI Global Publ., 2008. ISBN 9781599049663.
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On-line library catalogues
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Contact hours
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52
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Preparation for formative assessments (2-20)
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3
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Presentation preparation (report) (1-10)
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4
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Preparation for an examination (30-60)
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40
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Preparation for laboratory testing; outcome analysis (1-8)
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5
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Total
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104
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Prerequisites
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Knowledge - students are expected to possess the following knowledge before the course commences to finish it successfully: |
využívat znalosti z oblasti umělé inteligence získané absolvováním předmětu Umělá inteligence a rozpoznávání |
navrhnout a realizovat i složitější programové systémy s umělou inteligencí |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
vytvářet programy v procedurálních (min. v Javě a v C++) i neprocedurálních (min. v Prologu) programovacích jazycích |
využívat některé databázové systémy |
navrhovat a realizovat složitější programové systémy |
Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
N/A |
N/A |
N/A |
N/A |
N/A |
má znalosti z oblasti vytváření efektivních programových struktur a jejich snadného ladění |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
základních datových i programových struktur pro reprezentaci znalostí |
používání těchto struktur pro efektivní reprezentaci znalostí |
navrhování znalostních systémů, včetně aplikace agentových technologií a multiagentních systémů |
programové realizace výše uvedených systémů |
Skills - skills resulting from the course: |
navrhovat a programově realizovat efektivní reprezentaci znalostí |
navrhovat a programově realizovat jednoduché báze znalostí |
s využitím jednodušších databázových systémů realizovat báze dat |
navrhovat architekturu jednodušších znalostních systémů |
Competences - competences resulting from the course: |
N/A |
N/A |
vyhodnotit efektivitu realizovaných programových struktur a systémů |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
Combined exam |
Test |
Individual presentation at a seminar |
Skills - skills achieved by taking this course are verified by the following means: |
Skills demonstration during practicum |
Seminar work |
Group presentation at a seminar |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Lecture with visual aids |
Lecture supplemented with a discussion |
Practicum |
Laboratory work |
E-learning |
Multimedia supported teaching |
Task-based study method |
Self-study of literature |
Individual study |
Students' portfolio |
One-to-One tutorial |
Skills - the following training methods are used to achieve the required skills: |
Laboratory work |
Task-based study method |
Skills demonstration |
Competences - the following training methods are used to achieve the required competences: |
E-learning |
Textual studies |
Self-study of literature |
Individual study |
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