02285 Artificial Intelligence and Multi-Agent Systems

2024/2025

Course information
Kunstig intelligens og multiagent-systemer
English
7,5
MSc
Offered as a single course
General competence course (MSc), Computer Science and Engineering
Programme specific course (MSc), Autonomous Systems
Programme specific course (MSc), Computer Science and Engineering
Programme specific course (MSc), Human-Centered Artificial Intelligence
Technological specialization course (MSc), Autonomous Systems
Technological specialization course (MSc), Computer Science and Engineering
Technological specialization course (MSc), Human-Centered Artificial Intelligence
Spring F4A (Tues 13-17)
Campus Lyngby
Lectures, exercises, assignments and a large programming project.
13 weeks
F4A
Evaluation of exercises/reports
The assessment is based on 2 assignments during the course and a large final programming project. The final programming project is a group project, and the 2 other assignments will be either individual or group work. All group assignments are required to be individualised (stating exactly who did what). The course is evaluated as a whole.
7 step scale , internal examiner
01017. 02101. 02105. 02180. , or equivalent courses, including knowledge about graph search algorithms, search heuristics and a bit of predicate logic. Furthermore, the course requires experience with implementing non-trivial algorithms and larger software systems.
Thomas Bolander , Lyngby Campus, Building 322, Ph. (+45) 4525 3715 , tobo@dtu.dk
01 Department of Applied Mathematics and Computer Science
At the Studyplanner
Please contact the teacher for information on whether this course gives the student the opportunity to prepare a project that may participate in DTU´s Study Conference on sustainability, climate technology, and the environment (GRØN DYST). More infor http://www.groendyst.dtu.dk/english
General course objectives
This course introduces students to advanced techniques within artificial intelligence (AI), with particular focus on automated planning and multi-agent systems. The objective of the course is to become able to explain, analyse and implement advanced AI techniques.
Learning objectives
A student who has met the objectives of the course will be able to:
  • describe a number of the most prevalent techniques in artificial intelligence and multi-agent systems - both in overall terms and on a detailed technical level
  • compare and assess the appropriateness of various AI techniques within automated planning and multi-agent systems for solving a given concrete problem
  • combine different AI techniques in a theoretically sound and practically useful way
  • apply a given AI technique to a given concrete problem
  • clarify the general complications and pitfalls involved in practical uses of AI techniques
  • independently explore the literature relevant to a specific AI project within automated planning and multi-agent systems
  • implement non-trivial AI techniques in a relatively large software system
  • communicate results within the areas of the course in the style of a research conference contribution
Content
The course primarily focuses on topics within automated planning and multi-agent systems, but will also address other areas of AI (e.g. problem-solving by searching, knowledge representation and reasoning with logical agents).

The programming project concerns the design and implementation of advanced AI techniques in a simulated multiagent environment. The programming project is very open-ended and invites for the development of your own algorithms and multiagent architectures. The project is carried out in groups, and should result in a working system and a video in which you present the system and its underlying ideas in the style of research presentations at AI conferences.

In addition to the programming project there will be 2 smaller assignments during the course.
Last updated
02. maj, 2024