| Kursusnavn (dansk): | Modern AI for Games |
| Kursusnavn (engelsk): | Modern AI for Games |
| Semester: | Efterår 2010 |
| Udbydes under: | cand.it., medieteknologi og spil (mtg) |
| Omfang i ECTS: | 15,00 |
| Kursussprog: | Engelsk |
| Kursushjemmeside: | https://blog.itu.dk/MAIG-E2010/ |
| Min. antal deltagere: | 12 |
| Forventet antal deltagere: | 20 |
| Maks. antal deltagere: | 35 |
| Formelle forudsætninger: | Students must have completed a course on programming such as "Introductory Programming", or "Object-Oriented Programming" and "Efficient AI Programming." Having completed the "Game Development" course is a plus. This course will partly cover AI methodologies of the "Efficient AI Programming" module through a clearer game perspective and introduce state-of-the-art topics of advanced game AI. |
| Læringsmål: | After the course the students should be able to:
* Describe and theorize on the AI algorithms covered in the class.
* Identify tasks that can be tackled through advanced AI techniques
and select the appropriate technique for the problem under investigation.
* Compare the performance of different AI techniques and reflect on
their suitability for game AI development.
* Design and implement efficient and robust advanced AI algorithms.
* Work efficiently in groups and evaluate the algorithms in
commercial-standard game productions. |
| Fagligt indhold: | The primary goal of the course is the understanding, design,
implementation and use of nouvelle AI techniques for generating efficient
intelligent behaviors in games. Additional focus will be given to
state-of-the-art AI algorithms for improving gameplay experience
and game development procedures.
The course will, in part, cover the following topics (AI techniques and
problems):
AI techniques
o Finite-State Machines
o Behavior Trees
o Fuzzy Logic
o Evolutionary Algorithms
o Artificial Neural Networks
o Reinforcement Learning
o Hybrid Approaches
Tasks/Problems
o Pathfinding
o Non-Player Character AI
o Adaptation and Learning (off-line and on-line)
o Player and Player Experience Modeling
o Dynamic Difficulty Adjustment
o Affective Computational Intelligence and Games
o Procedural Content Generation |
| Læringsaktiviteter: | 14 weeks of teaching consisting of lectures, exercises and supervision
o 6 weeks of intensive lectures + mandatory individual assignment. The
mandatory individual assignment (6 page written report + source code)
will be
handed-in *October 15, 15:00* (*hard deadline*) to the instructor.
Please note that students will not be eligible for the final exam if
they fail on this assignment.
o 8 weeks of group project work with supervision (some lectures are
planed during this period). The group project report (written work +
source code + video production)
will be handed-in at the examination office by *December 15, 15:00*
(*hard deadline*).
Students are responsible for attending lectures (some of which will
likely be by outside guest speakers) and then working on their projects
independently. Besides the hours planned for lectures, tutorials, exercise
and supervision sessions are planned which complement the theory covered
during the lectures
and are necessary for meeting the learning objectives of the course.
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|
| Eksamensform og -beskrivelse: | X. experimental examination form (7-scale; external exam), 7-trins-skala, Ekstern censur External examiner, 7-point marking scale, B4: Oral examination with
written work but without time for preparation at the exam. The exam will
be 30 minutes long; 10 minutes will be given for project presentation
and 20 minutes for the Q+A and grade assessment sessions.
The mandatory individual assignment (6 page written report + source code) will be
handed-in *October 15, 15:00* (*hard deadline*) to the instructor.
Please note that students will not be eligible for the final exam if they fail on this assignment.
|
| Litteratur udover forskningsartikler: | To be announced |
| | |
Kursusoversigt