At the end of this course, students should know the main languages for knowledge representation used in artificial intelligence, as well as algorithms and systems that enable them to develop applications that take advantage of the represented knowledge. They should also realize the advantages of using some of these languages in modelling Data Bases and Information Systems.
Knowledge:
Know-how:
Soft-Skills:
The course has a first part, which focuses on languages for domain specification (sometimes also known as Ontology Languages), and reasoning methods and algorithms for these languages. These languages have application both in the area of Artificial Intelligence, and in the area of Data Bases and Information Systems. In the second part, the course focuses on languages and systems for knowledge representation in typical problems of Artificial Intelligence, particularly in languages for the representation of commonsense knowledge, and the representation of knowledge in problems of satisfaction, scheduling and planning.
Text Books
• Knowledge Representation and Reasoning by Ronald Brachman & Hector Levesque, Morgan Kaufmann 2004.
• Answer Set Solving in Practice by M. Gebser, R. Kaminski, B. Kaufmann, and T. Schaub. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan and Claypool, 2012.
• Handbook of Knowledge Representation edited by Frank van Harmelen, Vladimir Lifschitz and Bruce Porter, Elsevier 2007.
• The Description Logic Handbook: Theory, Implementation, and Applications. F. Baader, D. Calvanese, D. McGuinness, D. Nardi, and P. F. Patel-Schneider. Cambridge University Press, 2003.
Hours per credit | 28 | ||
Hours per week | Weeks | Hours | |
Aulas práticas e laboratoriais | 28.0 | ||
Aulas teóricas | 28.0 | ||
Avaliação | 4.0 | ||
Self study | 60.0 | ||
Project | 48.0 | ||
Total hours | 168 | ||
ECTS | 6.0 |