Popis předmětu - AE4M33RZN
AE4M33RZN | Advanced Methods for Knowledge Representation | ||
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Role: | Rozsah výuky: | 2P+2C | |
Katedra: | 13136 | Jazyk výuky: | EN |
Garanti: | Zakončení: | Z,ZK | |
Přednášející: | Kreditů: | 6 | |
Cvičící: | Semestr: | Z |
Webová stránka:
http://cw.felk.cvut.cz/doku.php/courses/ae4m33rzn/startAnotace:
This course aims to deepen understanding of knowledge representation principles beyond the predicate logic formalism. Firstly, the course presents ontologies and description logic, the principle elements of semantic web. Then, attention will be paid to statements whose validity varies in time. Uncertainty makes the next issue to be discussed. Modal logic extends the classical logic with additional modalities, namely, possibility, probability, and necessity. Probabilistic graphical models associate the classical probabilistic theory with the graph theory. Fuzzy sets allow to represent vagueness.Výsledek studentské ankety předmětu je zde: AE4M33RZN
Cíle studia:
To learn advanced formalisms for representation of structured and uncertain knowledge.Osnovy přednášek:
1. | Introduction frames and ontologies. | |
2. | Description logic language and its expressivity, interactions with rule-based systems. | |
3. | Description logic inference, tableuax method. | |
4. | Description queries forming and evaluation. Inconsistency in ontologies. | |
5. | Tractable fragments of description logic. Present and future of semantic web. | |
6. | Modal logic definitions and applications. | |
7. | Temporal logic definitions and applications. | |
8. | Uncertainty in knowledge-based systems role and representation. | |
9. | Uncertainty and conditional independence introduction to probabilistic networks. | |
10. | Probabilistic graphical models introduction, inference. | |
11. | Dynamic models applications of probabilistic networks. | |
12. | Fuzzy logic vagueness. | |
13. | Fuzzy logic operations. | |
14. | Fuzzy logic inference. |
Osnovy cvičení:
1. | Introduction, ontological editor Protege. | |
2. | OWL language modeling, examples. | |
3. | Inference engine Pellet. | |
4. | Query language SPARQL. | |
5. | The first assignment OWL ontology for a selected domain, difference between OWA and CWA. | |
6. | The first assignment autonomous working. | |
7. | The first assignment autonomous working. | |
8. | Conjunctive queries working with the ontology. | |
9. | Expert and knowledge-based systems with uncertainty. | |
10. | SW probabilistic modeling tools (Bayes Net Toolbox for Matlab, Bayesian Networks in Java). | |
11. | The second assignment implementation of a probabilistic model. | |
12. | The second assignment autonomous working. | |
13. | Fuzzy sets. | |
14. | Spare slot finishing, credits. |
Literatura:
[1] | Franz Baader , Diego Calvanese , Deborah L. McGuinness , Daniele Nardi , Peter F. Patel-Schneider, The Description Logic Handbook, Cambridge University Press, New York, NY, 2007. | |
[2] | Baader, F., Sattler U.: An overview of tableau algorithms for description logics ; Studia Logica, 69:5-40, 2001. | |
[3] | Charniak, E.: Bayesian Networks without Tears. AI Magazine 12(4): 50-63, 1991. | |
[4] | Pearl , J.: Causality: Models, Reasoning and Inference. Cambridge University Press, 2001. |
Požadavky:
Topics contained in courses A4B33ZUI and A0B01PSI.Poznámka:
Rozsah výuky v kombinované formě studia: 14p+6c |
Klíčová slova:
ontology, description logic, conditional independence, bayesian network, fuzzy set and operation.Předmět je zahrnut do těchto studijních plánů:
Plán | Obor | Role | Dop. semestr |
Stránka vytvořena 5.10.2024 17:50:26, semestry: Z/2024-5, Z/2025-6, L/2024-5, L/2023-4, připomínky k informační náplni zasílejte správci studijních plánů | Návrh a realizace: I. Halaška (K336), J. Novák (K336) |