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The goal of this subject is to introduce the basics of symbolic artificial intelligence. We will cover the algorithms of informed and uninformed state space, problem solving methods, reinforcement learning, knowledge representation and (sequential) decision making under uncertainty.

Viliam Lisý , Branislav Bošanský

Jaromír Janisch , Vojtěch Čermák , Petr Tomášek , Marko Sahan , Ondřej Kubíček

The students can gain at most **30 points** for homework assignments. In order to get the credit (zápočet), they have to submit each task for at least 5 points (before the penalisation for late submissions - penalisation does not prevent getting the credit) and gain at least **15 points** for home works overall.

Penalisation for late submission:

- less than 24h after the deadline – losing 20% points
- more than 24h after the deadline – losing 100% points

Task | Deadline | Points | Minimal points |
---|---|---|---|

Task 1: Path planning (A*) | 26/03/23 25:59 | 10 | 5 |

Task 2: Reinforcement Learning | 16/04/23 23:59 | 10 | 5 |

Midterm Test | 17-18/04/23 (your lab) | 15 | 0 |

Task 3: Playing a two-player game | | 10 | 5 |

**Always work on your assignments individually. Plagiarism is being detected and it is not tolerated.** If you have an objective reason for difficulties with finishing the assignment on time, contact us, please.

In the middle of the semester, there will be a test similar to the final exam for **15 points**.

- Getting the credit (zápočet)
- Passing the final exam
- The sum of the points for the final exam, midterm exam, and home works determines the final grade (50-59p. = E, …, 90-100p. = A).

The final exam is for up to 55 points:

- the exam is in the written form
- the students must gain at least 28 points to pass the exam
- the students with overall sum of scores over 80 for the whole subject will have to pass also brief oral examination to defend the grade
- the topics of the questions for the exam are given by the slides, however, the slides are
**not meant to be the primary study materials**and are not self-explanatory - you can use a calculator during the exam (not a mobile phone, no other materials)

Example past exam tests (since there was a larger change of topics in 2020/2021 and we are gradually updating the content, these can be outdated)

Exam dates:

- 2.6. KN-E:107 9:00 - 12:00
- 15.6. KN-E:301 9:00 - 12:00
- 26.6. KN-E:301 9:00 - 12:00
- 1 exam in September

Example of the questions for midterm test:

(relevant captures mention in individual lectures)

- [AIMA] Russel, S. a Norvig, P.: Artificial Intelligence: A Modern Approach (2nd edition), Prentice Hall, 2003
- příslušné kapitoly k dispozici na vyžádání

- [RLbook] Richard S. Sutton and Andrew G. Barto: Reinforcement Learning: An Introduction http://incompleteideas.net/book/the-book.html

courses/zui/start.txt · Last modified: 2023/05/26 12:49 by bosanbra