Artificial Intelligence (EAI)
Summer 2022
M.Sc. in Control and Robotics
(with JEMARO+)
Contents:
Meeting times and locations
Lecture:
Tuesday, 12:15-14:00, E&IT Faculty, room 528
Tutorial: Tuesday, 14:15-16:00, room 528, E&IT Faculty
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Teaching staff and contact info
prof. Włodzimierz Kasprzak (room 565)
Office: E&IT Faculty, Institute of Control and Computation Eng.
wlodzimierz.kasprzak@pw.edu.pl
Office hours: Monday, 12.00-13.00 (MS Teams, code: ghtadwu)
Phone: +22 234 7866
M.Sc. Maciej Stefańczyk (room 564)
maciej.stefanczyk@pw.edu.pl
Office hours:
Phone:
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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. Four parts are distinguished: reasoning in logic, problem solving and planning, inference with imperfect knowledge and learning.
Particular topics are: agents, logical inference, informed search, CSP, planning, fuzzy reasoning, Bayesian nets, dynamic Bayesian nets, decision tree learning, reinforcement learning, classifier learning.
Prerequisities
Students are expected to have the following background:
- knowledge at the level of BSc-studies in logic, probability theory, statistics and computer science -
in the area of programming, algorithmics and data structures.
- knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program, in a high-level programming language.
Course materials
Lecture notes are posted on the course's web page.
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.
Students are collecting assessment points. They come partly from a continuous assessment in the semester time (tutorial and a midterm test) and from a final test. The assessment method includes:
- tutorial: 0 - 30 pts.;
- midterm test: 0 - 30 pts.;
- final test: 0 - 40 pts.
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 (JEMARO+)
|
100- 90
|
89-80
|
79-70
|
69-65
|
64-60
|
59 or less
|
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Lecture and tests
Lecture schedule (tentative):
- [1.03]: 1) Agent-based systems.
- [8.03]: 2) Propositional logic.
- [15.03]: 3) First-order (predicate) logic.
- [22.03]: 4) Search I: state space search.
- [29.03]: 5) Search II: path search.
- [5.04]: 6) Search III: solution search.
- [12.04]: 7) Action planning.
- [19.04]: free day
- [26.04] Test 1 (sec. 1-7).
- [3.05]: free day
- [Wednesday 4.05]: 8) Inexactness and uncertainty.
- [10.05]: 9) Bayesian nets.
- [17.05]: 10) Inference in Bayesian nets.
- [24.05]: 11) Dynamic Bayesian nets
- [31.05]: 12) Learning (estimation, regression)
- [7.06]: 13) Decision and strategy learning
- [14.06] Final test (part II, sec. 8-13)
Tests:
- [26.04] First test
- [14.06] Second test
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Textbooks and suggested readings
Textbooks:
- [RN] S.Russel, P.Norvig: Artificial Intelligence. A Modern Approach. Prentice Hall, 2002 (2nd ed.), 2011 (3d ed.) [Chapters 1-5, 7-9, 11-15].
- [WK] W.Kasprzak: Artificial Intelligence. Lecture notes. WUT, 2010-2021.
Supporting WWW page:
aima.cs.berkeley.edu
Additional readings:
- D. Barber: Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012-2020, http://www.cs.ucl.ac.uk/staff/d.barber/brml/
- M. Flasiński: Introduction to Artificial Intelligence, e-book: Springer, 2016, https://www.springer.com/us/book/9783319400204
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Tutorial
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:
- [8.03] E1. A.I. agents.
- [22.03] E2. Predicate logic.
- [5.04] E3. Search in problem solving.
- [12.04] E4. Search (cont.) Planning.
- [19.04]: free day
- [10.05] E5. Fuzzy logic. Bayesian Nets.
- [24.05] E6. Dynamic Bayesian Nets.
- [7.06] E7. Learning.
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Last modification:
28.02.2022