|Kursusnavn (dansk):||Modern AI for Games |
|Kursusnavn (engelsk):||Modern AI for Games |
|Semester:||Efterår 2017 |
|Udbydes under:||cand.it., spil (games) |
|Omfang i ECTS:||15,00 |
|Min. antal deltagere:||1 |
|Forventet antal deltagere:||0 |
|Maks. antal deltagere:||40 |
|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 modern artificial intelligence (AI) and computational intelligence (CI) 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 partly cover the following topics (AI techniques and problems):
o Finite-state machines
o Behaviour trees
o Evolutionary algorithms
o Artificial neural networks
o Reinforcement learning
o Hybrid approaches
o Non-player character AI
o Adaptation and learning (off-line and on-line)
o Player behaviour modelling
o Player experience Modeling
o Dynamic difficulty adjustment
o Social simulation
|Læringsaktiviteter:||14 ugers undervisning bestående af forelæsninger, øvelser og vejledning|
o 6 weeks of intensive lectures + mandatory individual assignment (see below).
o 8 weeks of group project work with supervision (some lectures are planned during this period).
Students are responsible for attending lectures (some of which will likely be by outside guest speakers) and then working on their projects independently (individual mandatory assignment) or in groups. Besides the hours planned for lectures, tutorial, 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. You will practice presenting your work during the course in order to prepare for the oral exam.
Lectures provide theoretical foundations and walk-through examples of relevant AI algorithms while exercises focus on students discussing and implementing the central algorithms themselves.
|Obligatoriske aktivititer:||During this course students will be required to hand in an individual mandatory assignment (6 page written report + source code), that needs to be completed/approved before being eligible for the examination.
The mandatory assignment deadline is posted in learnIT.
Be aware: The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt
|Eksamensform og -beskrivelse:||D2G Aflevering med mundtlig eksamen der supplerer projekt. Delt ansvar for projekt., (7-scale, external exam)|
The total duration of the oral examination is 30 minutes per examinee.
The written work is a group project report (written work + source code + video production). Group size is normally 2-3 people.
|Litteratur udover forskningsartikler:||. |