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Lecturer(s)
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Šilhavý Radek, prof. Ing. CSc.
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Course 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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Learning activities and teaching methods
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Lecture supplemented with a discussion, E-learning, Multimedia supported teaching, Students' portfolio, One-to-One tutorial, Laboratory work, Task-based study method, Individual study, Self-study of literature, Lecture with visual aids, Practicum
- Contact hours
- 52 hours per semester
- Preparation for formative assessments (2-20)
- 3 hours per semester
- Preparation for laboratory testing; outcome analysis (1-8)
- 5 hours per semester
- Preparation for an examination (30-60)
- 40 hours per semester
- Presentation preparation (report) (1-10)
- 4 hours per semester
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| prerequisite |
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| Knowledge |
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| využívat znalosti z oblasti umělé inteligence získané absolvováním předmětu Umělá inteligence a rozpoznávání |
| navrhnout a realizovat i složitější programové systémy s umělou inteligencí |
| Skills |
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| 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 |
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| N/A |
| N/A |
| N/A |
| N/A |
| N/A |
| má znalosti z oblasti vytváření efektivních programových struktur a jejich snadného ladění |
| learning outcomes |
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| Knowledge |
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| 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 |
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| 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 |
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| N/A |
| N/A |
| vyhodnotit efektivitu realizovaných programových struktur a systémů |
| teaching methods |
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| Knowledge |
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| 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 |
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| Laboratory work |
| Task-based study method |
| Skills demonstration |
| Competences |
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| E-learning |
| Textual studies |
| Self-study of literature |
| Individual study |
| assessment methods |
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| Knowledge |
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| Combined exam |
| Test |
| Individual presentation at a seminar |
| Skills |
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| Skills demonstration during practicum |
| Seminar work |
| Group presentation at a seminar |
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Recommended literature
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Abdoullaev, A. Reality, Universal Ontology and Knowledge Systems. IGI Global Publ., 2008. ISBN 9781599049663.
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Brachman, R.J.; Levesque, H.J. Knowledge Representation. Elsevier, 2004. ISBN 1558609326.
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Dvořák, J. Expertní systémy. Skriptum VUT Brno, 2004.
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Geisler, E. Knowledge and Knowledge Systems. IGI Global Publ, 2007. ISBN 9781599049182.
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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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Stefik, M. Introduction to Knowledge Systems. Morgan Kaufman Publ., 1995. ISBN 155860166X.
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