Artificial Intelligence

EMARO course, Summer 2009


Meeting times and locations

Tuesday, 13:15-15:00 (i.e. 1:15 p.m.- 3:00 p.m.) (two weeks out of three or every second week), room 520, E&IT Faculty

Tuesday, 13:15-15:00 (every third week), room 520, E&IT Faculty

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Teaching staff and contact info

Professor Włodzimierz Kasprzak
Office: room 554, E&IT Faculty, Institute of Control and Computation Eng.
Office hours: Wednesday, 12-14 (i.e. 12 a.m.-2 p.m.)

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Short course description

Course objectives This course provides an introduction to artificial intelligence from the perspective of robotics. Four parts are distinguished: logical systems, problem solving and planing, Bayesian networks and machine learning. Topics include: agent systems, inference, search, planing, Bayesian networks, dynamic Bayesian networks, reinforcement learning, Bayesian learning.

Students are expected to have the following background:

Course materials
Lecture notes will be posted periodically on the course web site. Selected chapters from the textbook below are recommended as optional reading.

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Marks and Grading

Assessment will be marked out of a hundred. The marks equate to ECTS grades as given below:
mark 100- 90 89-80 79-70 69-65 64-60 59 or less
Students are collecting assessment points. They come partly from a continuous assessment in the semester time and from a final exam. The assessment method is: In addition to satisfying the above assessment requirements, each student must satisfy the attendance requirements. There is an obligatory attendance of tutorial and an optional attendance of the lecture. The Pass mark for this course will be set at 60 pts. Credits will be awarded to candidates who pass this course.

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Lecture schedule (20 h):

  1. [29.04] Half-time test, 14.15 - 15.30. .

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Textbooks and suggested readings

  1. [RN] S.Russel, P.Norvig: Artificial Intelligence. A Modern Approach. Prentice Hall, Second Edition, 2002.
Supporting WWW page:

Suggested Readings
(Week) Topic Readings
(Week 1) L1. [RN, ch. 1, 2]
(Week 2) L2. [RN, ch. 7,8 ]
(Week 3) Tuto 1.
(Week 4) Tuto 1 (cont.). L3. [RN, ch. 9]
(Week 5) L3 (cont.) [RN, ch. 9]
(Week 6) Tuto 2.
(Week 7) L4. [RN, ch. 3, 4]
(Week 8) L5. [RN, ch. 5]
(Week 9) Tuto 3. Midterm test.
(Week 10) L6. [RN, ch. 11]
(Week 11) L7. [RN, ch. 13-15]
(Week 12) Tuto 4. L8. [RN, ch. 16]
(Week 13) L9. [RN, ch. 21]
(Week 14) Tuto 5.
(Week 15) 5.06, Exam.

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During the exercises some tasks will be solved, which are related to the algorithms presented during the lecture. Students will also select their homework problems. The examination will cover smilar tasks as solved during exercises.

Exercises (12h)

  1. [12.03; 17.03] E1. Logical agents (AI-E1.pdf), Exercises 1 (AI-E1-sol.pdf) with solutions
  2. [31.03] E2. Inference in logic. (AI-E2.pdf) , Exercises 2 (AI-E2-sol.pdf) with solutions
  3. [28.04] E3. Search. (AI-E3.pdf) , Exercises 3 (AI-E3-sol.pdf) with some solutions.

    [29.04] Midterm test. 14.15-15.30

  4. [19.05] E4. Planning. Bayesian Networks. (AI-E4.pdf)
  5. [2.06] E5. Inference in BN. DBN. (AI-E5.pdf)

    [5.06, time 12.15] Final test

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Project work

On request.

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W. Kasprzak.
Last modification: 12.06.2009.