Computer Science, Multi-Agent Systems, Second Cycle, 7.5 credits

Aims and objectives

General aims for second cycle education

Second-cycle courses and study programmes shall involve the acquisition of specialist knowledge, competence and skills in relation to first-cycle courses and study programmes, and in addition to the requirements for first-cycle courses and study programmes shall

• further develop the ability of students to integrate and make autonomous use of their knowledge
• develop the students' ability to deal with complex phenomena, issues and situations, and
• develop the students' potential for professional activities that demand considerable autonomy, or for research and development work.

(Higher Education Act, Chapter 1, Section 9)

Course objectives

Knowledge and understanding
After the course the participant should be able to

- explain fundamental concepts in the area of multiagent systems,
- discuss different methods and architectures for constructing single agents and complete multiagent systems, and
- account for different forms of distributed decision making with advantages and disadvantages.

Applied knowledge and skills
After the course the participant should be able to

- plan and implement qualifying tasks with appropriate methods, within a given time frame,
- account for and discuss findings and the underlying knowledge
- understand, summarize and discuss scientific literature, and
- develop and evaluate software for solving complex distributed problems using multiagent system techniques.

Making judgments and attitudes
After the course the participant should be able to

- discuss the computational and representational considerations that need to be done for systems with multiple robots or other applications of mutliagent systems.

Main content of the course

The course has the following contents

- basic introduction to multiagent systems, concepts and appropriate application areas
- agent and multiagent system architectures
- task allocation and result sharing
- distributed decision making
- multi-agent planning and coordination
- practical application of appropriate algorithms for intelligent systems that consists of multiple agents with problem formulation, data analysis, implementation and appropriate presentation, presentation and report writing.

Teaching methods

Lectures, theoretical and practical exercises to be done alone or in a group according to the teacher’s instruction.

If there is a too small number of students, lectures can be replaced by individual supervision.

Students who have been admitted to and registered on a course have the right to receive tuition and/or supervision for the duration of the time period specified for the particular course to which they were accepted (see, the university's admission regulations (in Swedish)). After that, the right to receive tuition and/or supervision expires.

Examination methods

Multiagent-system, Theory, 4 credits (Code: A002)
Written examination. If there is a too small number of students, the written examination can be replaced by an oral examination.

Multiagent-system, Practice, 3.5 credits (Code: A003)
Written and oral presentation of assignments.

For students with a documented disability, the university may approve applications for adapted or other forms of examinations.

For further information, see the university's local examination regulations (in Swedish).

Grades

According to the Higher Education Ordinance, Chapter 6, Section 18, a grade is to be awarded on the completion of a course, unless otherwise prescribed by the university. The university may prescribe which grading system shall apply. The grade is to be determined by a teacher specifically appointed by the university (an examiner).

According to regulations on grading systems for first- and second-cycle education (vice-chancellor's decision 2019-01-15, ORU 2019/00107), one of the following grades is to be used: fail, pass, or pass with distinction. The vice-chancellor or a person appointed by the vice-chancellor may decide on exceptions from this provision for a specific course, if there are special reasons.

Grades used on course are Fail (U), Pass (G) or Pass with Distinction (VG).

Multiagent-system, Theory
Grades used are Fail (U), Pass (G) or Pass with Distinction (VG).

Multiagent-system, Practice
Grades used are Fail (U) or Pass (G).

For further information, see the university's local examination regulations (in Swedish).

Comments on grades

The grade for the overall course is determined by the grade of code A002, given that code A003 is passed.

The grades of the course are translated into the ECTS schema

Specific entry requirements

First-cycle degree of 180 credits, with Computer Science as the main field of study, and at least 15 credits in mathematics (analysis and algebra). The applicant must also have qualifications corresponding to the course "English 6" or "English B" from the Swedish Upper Secondary School.

OR

First-cycle degree of 180 credits, and at least 30 credits in mathematics (analysis and algebra), as well as at least 15 credits in Computer Science or Informatics (which includes programming). The applicant must also have qualifications corresponding to the course "English 6" or "English B" from the Swedish Upper Secondary School.

For further information, see the university's admission regulations (in Swedish).

Transfer of credits for previous studies

Students who have previously completed higher education or other activities are, in accordance with the Higher Education Ordinance, entitled to have these credited towards the current programme, providing that the previous studies or activities meet certain criteria.

For further information, see the university's local credit transfer regulations (in Swedish).

Other provisions

The course is given in English.

Reading list and other teaching materials

Reference Literature

Wooldrige, Michael (2009)
An Introduction to MultiAgent Systems
Wiley, latest edition

More material will be available during the course.