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Course, academic year 2017/2018
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Seminar on Artificial Intelligence I - NAIL004
Czech title: Seminář z umělé inteligence I
Guaranteed by: Department of Theoretical Computer Science and Mathematical Logic (32-KTIML)
Faculty: Faculty of Mathematics and Physics
Actual: from 2017
Semester: winter
E-Credits: 3
Hours per week, examination: winter s.:0/2 C [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech, English
Teaching methods: full-time
Additional information: http://ktiml.mff.cuni.cz/~bartak/ui_seminar/
Guarantor: prof. RNDr. Roman Barták, Ph.D.
Class: Informatika Mgr. - volitelný
Classification: Informatics > Theoretical Computer Science
Annotation -
Last update: T_KTI (27.09.2002)

Referative seminar about actual theoretical and practical questions in a field of Artificial Intelligence, based on published papers.
Aim of the course -
Last update: prof. RNDr. Roman Barták, Ph.D. (06.10.2017)

Using the form of oral presentations, the seminar teaches students how to watch recent results in AI and how present research results or alternatively how to solve some AI problems.

Course completion requirements -
Last update: prof. RNDr. Roman Barták, Ph.D. (06.10.2017)

Credit is given for presentation of a student and active participation at seminars (at least 80% seminars attended). The credit cannot be repeated.

Literature -
Last update: prof. RNDr. Roman Barták, Ph.D. (06.10.2017)

Conference Proceedings

  • AAAI Conference on Artificial Intelligence
  • International Joint Conference on Artificial Intelligence
  • European Conference on Artificial Intelligence

Journals

  • Artificial Intelligence
  • Journal of Artificial Intelligence Research
Teaching methods -
Last update: prof. RNDr. Roman Barták, Ph.D. (06.10.2017)

Seminar with oral reports, where students present own results or results from literature. It may involve solving a selected AI problem.

Syllabus -
Last update: T_KTI (06.05.2010)

Topic of relevant papers and discussion topics:

Knowledge representation, knowledge engineering.

Constraint satisfaction, inference techniques.

Problem solving, games, automated planning.

Machine learning, agent-based systems.

Applications of AI.

 
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