Systems with Artificial Intelligence
- Department of Cybernetics
The content of the subject aims at introduction to the basic goals of artificial intelligence, its key methods and examples of most frequent practical applications. The subject overviews the basic techniques to develop generally intelligent systems and it introduces its selected concrete representants.
The topics contain methods of state space search, knowledge and its representation, automated logical reasoning with possible uncertainty, machine learning, distributed artificial intelligence or evolutionary algorithms. In the practical parts students get familiar with applications of knowledge-based, multiagent or robotic systems and data mining.
The students need to have previous knowledge of mathematical theory of probability.
- Syllabus of lectures:
1. Goals of artificial intelligence. State space and problem solving by search.
2. Non-informed and informed methods of state space search.
3. Evolutionary algorithms and artificial life.
4. Knowledge, its acquisition and representation. Knowledge engineering, knowledge management.
5. Knowledge-based and expert systems. Probabilistic representation of uncertainty and reasoning with uncertainty.
6. Fuzzy logic, Bayesian networks.
7. Possibility theory, Dempster-Shafer theory.
8. Semantic nets and frames, ontologies. Topic Maps. Conceptual graphs, semantic annotation of electronic resources.
9. Description logic, inference. Semantic Web - XML, RDF, OWL and SWRL.
10. Adaptive and self-learning algorithms.
11. Learning from examples - basic methods.
12. Application of artificial intelligence: expert and multi-agent systems, robotics, biological and industrial data mining.
- Syllabus of tutorials:
1. Solving simple tasks.
2. Problem solving in state space - non-informed methods, informed methods.
3. Evolutionary systems as a tool for optimization - demo
4. Standalone work.
5. State space search task presentation and submission.
6. Expert systems - FELexpert - introduction.
7. Expert systems - FELexpert - solving tasks.
8. Ontology - design.
9. Ontology - implementation (Protege, SWOOP).
10. Machine learning - working with WEKA tool.
11. Solving tasks of machine learning - learning from examples.
12. Learning and optimization task presentation and submission.
13. Credits. Reserve.
- Study Objective:
Goals of study comprise:
- introduction to the basic goals of artificial intelligence, its key methods and examples of most frequent practical applications,
- gaining overview of the the basic techniques to develop generally intelligent systems, and
- gaining practical experience with applications of problem solving, knowledge-based systems, machine learning and optimization.
- Study materials:
1. John F. Sowa: „Knowledge Representation: Logical, Philosophical, and Computational Foundations“, Brooks Cole Publishing Co., Pacific Grove, CA, 2000.
2. Steffen Staab, Rudi Struder: „Handbook on Ontologies“, Springer, 2004
3. Grigoris Antoniou, Frank van Harmelen: „A Semantic Web Primer“, MIT Press, London, 2004
4. XML Tutorial, http://www.w3schools.com/xml/
5. Sean Bechhofer, Ian Horrocks and Peter F. Patel-Schneider: „Tutorial on OWL“, http://www.cs.man.ac.uk/~horrocks/ISWC2003/Tutorial/
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans: