Artificial Intelligence (EAI)

Summer 2018

M.Sc. in Control and Robotics
European Masters in Advanced Robotics+ (EMARO+)


Meeting times and locations

Tuesday, 14:15-16:00, room 528, E&IT Faculty

Tutorial: selected Tuesdays, 16:15-18:00, room 528, E&IT Faculty

[go to top]

Teaching staff and contact info

prof. Włodzimierz Kasprzak (room 565)
Office: E&IT Faculty, Institute of Control and Computation Eng.
Office hours: Monday, 10.15-12
Phone: +22 234 7866

Maciej Stefańczyk (room 564)

[go to top],

Short course description

Course objectives This course provides an introduction to artificial intelligence from the perspective of robotics. Thus, the focus is on agent-based system design. Three parts are distinguished: reasoning in logic, problem solving and planning, inference with imperfect knowledge. Particular topics are: agents, logical inference, informed search, CSP, planning, fuzzy reasoning, Bayesian nets, dynamic Bayesian nets, learning.

Students are expected to have the following background:

Course materials
Lecture notes are posted on the course's web page. Selected chapters from the textbook below are recommended as optional reading.

[go to top]

Marks and Grading

Assessment will be marked out of a hundred. Students are collecting assessment points. They come partly from a continuous assessment in the semester time (exercises and a midterm test) and from a final test. The assessment method assumes: There is an obligatory attendance of exercises and an optional attendance of lectures. Credits will be awarded to candidates who pass this course. The marks equate to local and ECTS grades as given below:
ECTS grade A B C D E F/FX
Local grade 5 4.5 4 3.5 3 2
Mark (Control & Robotics) 100- 91 90-81 80-71 70-61 60-51 50 or less
Mark (EMARO+) 100- 90 89-80 79-70 69-65 64-60 59 or less

[go to top]

Lecture and tests

Lecture schedule (tentative):

  1. [17.04, time 14.15] Midterm test (sec. 1-6)

  2. [12.06, time 14.15] Final test (sec. 7-12)

    Retake test:

  3. [19.06, time 12.15 !] Retake (all sections 1-12)

[go to top]

Textbooks and suggested readings

  1. [RN] S.Russel, P.Norvig: Artificial Intelligence. A Modern Approach. Prentice Hall, 2002 (2nd ed.), 2011 (3d ed.)
  2. [WK] W.Kasprzak: Artificial Intelligence. Lecture notes. WUT, 2010-16.
Supporting WWW page:

Suggested Readings
(Week) Topic from [Russel, Norvig: AIMA]
(Week 1) L1. [RN, ch. 1, 2]
(Week 2) L2. E1 [RN, ch. 7, 8]
(Week 3) L3. [RN, ch. 9]
(Week 4) L4. E2 [RN, ch. 3]
(Week 5) L5. E3. [RN, ch. 4]
(Week 6) L6. E4. [RN, ch. 5]
(Week 7) Midterm test
(Week 8) L7. [RN, ch. 11]
(Week 9): L8. E5 [RN, ch. 12]
(Week 10): L9. [RN, ch. 13]
(Week 11) L10. E6 [RN, ch. 13, 14]
(Week 12) L11. E7 [RN, ch. 14]
(Week 13) L12. E8. [RN, ch. 15]
(Week 14) Final test
(Week 15) Retake test

[go to top]


In the tutorials, computational tasks will be solved, which are related to the algorithms presented in the lecture. The test and exams deal with tasks similar to the ones solved during tutorials.

Tutorial schedule:

[go to top]

Last modification: 20.02.2018