AI for Games

1.

Subject title

AI for Games

Вештачка интелигенција за игри

2.

Code

F23L3S153

3.

Study program

Software engineering and information systems, Примена на информациски технологии, Софтверско инженерство и информациски системи, Компјутерски науки, Компјутерско инженерство, Интернет, мрежи и безбедност, Информатичка едукација, Примена на информациски технологии, Софтверско инженерство и информациски системи, Компјутерски науки, Компјутерско инженерство, Интернет, мрежи и безбедност, Software engineering and information systems, Стручни студии за програмирање, Стручни студии за програмирање,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Прв циклус

6.

Academic year / semester

3 / Летен

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Андреа Кулаков ворн. проф. д-р Петре Ламески

9.

Prerequisites for enrollment

Алгоритми и податочни структури или Примена на алгоритми и податочни структури или Вештачка интелигенција

10.

Subject goals and competencies:

Students will understand the current issues and techniques in academic and industry game AI, while gaining, in the same time, hands-on experience in working with different toolboxes of game AI techniques used in the gaming industry today. They will be able to better understand the relationship between game AI and aesthetics, narrative, and player experience, thus obtaining an increased engagement and enjoyment of the players.

11.

Subject content:

The course deals with techniques of both AI behaviour in games and AI playing like humans. These techniques includе ways how game programs can learn responses and generate plans and movements based on players’ actions. Different traditional video games are analyzed, includiung arcade action games, strategy games, role-playing games, and other genres. Practically creating AI bots and procedural content generation systems. Other techniques covered include state- and goal-based behavior, inter-player communication, individual and team AI, graph theory, search, planning and optimization, triggers, scripting, finite state machines, perceptual modeling, goal evaluation. Analyzing and creating an engaging narative that involves AI in games. Potential function based movements: a technique that handles chasing, evading swarming, and collision avoidance simultaneously Basic pathfinding and waypoints, including an entire chapter devoted to the A* pathfinding algorithm AI scripting Rule-based AI: learn about variants other than fuzzy logic and finite state machines Experience programming game AI with Finite State Machines Experience programming game AI for team-based Game AI Experience programming game AI for real-time strategy games Experience programming game AI for arcade games

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

30 + 45 + 15 + 15 + 75 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

30 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

45 hours

16.

Other forms of activities

16.1.

Project tasks

15 hours

16.2.

Independent tasks

15 hours

16.3.

Homework

75 hours

17.

Grading method

17.1.

Tests

10 points

17.2.

Seminar work / project (presentation: written and oral)

15 points

17.3.

Activities and learning

10 points

17.4.

Final exam

50 points

18.

Grading criteria (points / grade)

up to 50 points

5 (five) (F)

from 51 to 60 points

6 (six) (E)

from 61 to 70 points

7 (seven) (D)

from 71 to 80 points

8 (eight) (C)

from 81 to 90 points

9 (nine) (B)

from 91 to 100 points

10 (ten) (A)

19.

Condition for signature and taking final exam

Редовна посета на наставата, навремено предадени домашни и презентација на проектна задача

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature