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Instructors: Dr. Matthias Kerzel
Event type:
Lecture
Displayed in timetable as:
WV - VL
Hours per week:
2
Credits:
4,0
Language of instruction:
English
Min. | Max. participants:
- | 60
Comments/contents:
The lecture 21/22 will again be held online over video chat and collaboration tools because of the Corona measures. The exact procedure and implementation will be discussed with the students in the first lecture.
Processing knowledge and representing knowledge are the central requirements for artificial intelligence systems. Many tasks in computer science, in particular those dealing with natural and inherently noisy environments, involve high degrees of complexity in actually perceiving, understanding and transferring available information. This lecture will present methods for processing and representing knowledge from a theoretical as well as from an application-oriented perspective. The theoretical concepts are exemplified by the areas of common sense reasoning, intelligent processing of imprecise or uncertain information, intelligent planning, language, and intelligent agents - for virtual agents and real robots as well as for autonomous individuals and multi-agent systems. Recent practical realizations are providing a transfer to knowledge-based software solutions and to robotic systems for day-by-day tasks.
Learning objectives:
Advanced knowledge of concepts and methods in the area of knowledge processing including knowledge representations and integration in realistic application scenarios. Preparation for further modules focussing on knowledge processing with neural networks, language processing, computational vision, robotics, and human-computer interaction as well as projects and master theses.
Didactic concept:
Lecture with discussions complemented with the associated seminar.
Literature:
- Russell, S., Norvig, P. Artificial Intelligence: An Modern Approach. Upper Saddle River, NJ: Prentice Hall - Pearson, 2010.
- Van Harmelen, F., Lifschitz, V., Porter, B. eds. Handbook of knowledge representation. Elsevier, 2008.
- Rojas, R. (2013). Neural networks: a systematic introduction. Springer Science & Business Media.
- Croft, W. B., Metzler, D., & Strohman, T. (2010). Search engines: Information retrieval in practice (p. 88). Reading: Addison-Wesley. (Chapters 4, 7 & 8)
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