DS250 Game AI

Code
DS250

Subject
Game AI

ECTS (European Credit Transfer System)
10.00

Course leader
Tomas Sandnes

External examiner
?

Date of approval


Aim
The course aims to give an introduction to artificial intelligence in relation with games, e.e. the building blocks that make up AI in the various types or genres of games. The students will also gain practical experience with implementing artificial intelligence in a game setting.

Prerequisites
The course build on basic skills in object oriented programming in Java.

Competence objectives

At completion of the course the students will have knowledge about:

  • Agents and what characterises a Virtual Player Agent and a Character Agent
  • Finite State Machines and variants of it
  • Blackbox and Whitebox implementation i an AI context
  • Scripting in AI and its possibilities
  • Rule based AI systems
  • Pathfinding, including benefits and disadvantages of types of algorithms used in the development of games such a A*, Dijkstra and Best-First Search
  • Pathfinding for groups of agents
  • Solutions that optimalise performance (Start- and End-Point Check, Zone Mapping)
  • Hierarchical pathfinding: Intermediate Destinations and Pruning
Structure
The course is taught with a series of lectures as well as lab exercises and discussions. There are twelve sessions of which five are teacher-led.

Curriculum

See the literature list

Grading system
Bokstavkarakter / Letter grade

Diploma supplement text
The course has given the students knowledge of and skills in artificial intelligence as it is used in computer games. The students have learned about Agents, Finite State Machines and Pathfinding (including A* algorithms), and have learned to optimalise the use of AI for games. The course has been taught with a combination of theory and practice.




Literature:
Tittel Forfatter Forlag Utgitt år ISBN Utgave Kommentar Type Litteratur
Game Development Essentials: Game Artificial Intelligence   John Ahlquist, Jeannie Novak   Thomson Delmar Learning   2007  978-1418038571      Bok  Pensum 

Vurdering / Assessment

Percentual weighting (%):
100

Type of assessment:
Skriftlig / Written

Help:
Ingen / None

Duration:
3 timer

Semester:
Vår / Spring