Artificial Intelligence in Games (3 cr)
Code: TT00CB18-3004
General information
- Enrollment
-
01.12.2023 - 31.01.2024
Registration for the implementation has ended.
- Timing
-
01.01.2024 - 31.07.2024
Implementation has ended.
- Number of ECTS credits allocated
- 3 cr
- Local portion
- 3 cr
- Mode of delivery
- Contact learning
- Unit
- Teknologia
- Teaching languages
- Finnish
- Degree programmes
- Bachelor’s Degree in Business Information Technology
- Teachers
- Mikko Romppainen
- Groups
-
TTK22SPORaaheTTK22SPORaahe
-
TTK22SPRaaheTTK22SPRaahe
- Course
- TT00CB18
Realization has 7 reservations. Total duration of reservations is 50 h 30 min.
| Time | Topic | Location |
|---|---|---|
|
Mon 08.04.2024 time 08:15 - 16:00 (7 h 45 min) |
Itsenäistä työskentelyä_Game Programming III_Artificial Intelligence in Games |
Raahe_Lybe_1_ATK
Raahe_Lybe_1_ATK
|
|
Mon 15.04.2024 time 08:15 - 16:00 (7 h 45 min) |
Itsenäistä työskentelyä_Game Programming III_Artificial Intelligence in Games |
Raahe_Lybe_1_ATK
Raahe_Lybe_1_ATK
|
|
Mon 22.04.2024 time 08:15 - 16:00 (7 h 45 min) |
Itsenäistä työskentelyä_Game Programming III_Artificial Intelligence in Games |
Raahe_Lybe_1_ATK
Raahe_Lybe_1_ATK
|
|
Mon 29.04.2024 time 08:15 - 16:00 (7 h 45 min) |
Itsenäistä työskentelyä_Game Programming III_Artificial Intelligence in Games |
Raahe_Lybe_1_ATK
Raahe_Lybe_1_ATK
|
|
Mon 06.05.2024 time 10:00 - 15:00 (5 h 0 min) |
Itsenäistä työskentelyä_Game Programming III_Artificial Intelligence in Games |
Raahe_Lybe_1_ATK
Raahe_Lybe_1_ATK
|
|
Mon 13.05.2024 time 08:15 - 16:00 (7 h 45 min) |
Itsenäistä työskentelyä_Game Programming III_Artificial Intelligence in Games |
Raahe_Lybe_1_ATK
Raahe_Lybe_1_ATK
|
|
Mon 20.05.2024 time 08:15 - 15:00 (6 h 45 min) |
Itsenäistä työskentelyä_Game Programming III_Artificial Intelligence in Games |
Raahe_Lybe_1_ATK
Raahe_Lybe_1_ATK
|
Objective
Students will have basic knowledge of different AI techniques used in games. Students will be able to select and implement specific AI techniques required in a game.
Content
Introduction to game AI
Finite state machines
Path finding
Game genre specific AI techniques
Learning AI
Distributed AI, Team AI and crowd techniques
Evaluation scale
0 - 5
Assessment criteria, excellent (5)
The students are able to apply their learning to different tasks in a variety of ways.
Assessment criteria, good (3)
The students are able to use the methods they learn during the course appropriately.
Assessment criteria, satisfactory (1)
The students know and are proficient in the basic concepts of the course.
Prerequisites
Data Structures and Algorithms