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ID2209 Distributed Artificial Intelligence and Intelligent Agents 7.5 credits

This course introduces fundamental principles and techniques of Distributed Artificial Intelligence (DAI), as well as the usage of such techniques for creating applications in distributed computing environments. Central to the course are the concepts of "intelligent agents", as a paradigm for creating autonomous software components, and “multi-agent systems” as a way of providing coordination and communication between individual autonomous software components.

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Headings with content from the Course syllabus ID2209 (Autumn 2021–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

  • Introduction and basic concepts for DAI (distributed artificial intelligence).
  • Coordination methods general models, joint coordination techniques, organizational structures, information exchange on the metalevel, multi-agent planning, explicit analysis and synchronisation. 
  • Negotiation methods: principles, protocols, production sequencing as negotiations, conventions for automatic negotiations. 
  • Interoperability: Methods for interoperation of software, speech acts, KQML, FIPA. 
  • Multi-agent architectures: Low-level architectural support, DAI-testbeds, agent oriented software development. 
  • Agent theory: Fundamentals of modal logic, the BDI architecture. 
  • Agent architectures: deliberative, reactive and hybrid architectures. 
  • Mobile agents: requirements, implementation, safety for mobile agents, environments for mobile agents. Agent typology and technical questions. Applications.
  • Practical part of the course that contains exercises and a project that includes implementation of a multi-agent system.

Intended learning outcomes

After passing the course, the student shall be able to

  • formulate definitions of the most important concepts and the methods for intelligent agents and multi-agent systems
  • evaluate and use the most important concepts and the methods in the area for intelligent agents and multi-agent systems.

Literature and preparations

Specific prerequisites

No information inserted

Recommended prerequisites

Knowledge of Java is desirable.


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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F


  • ANN1 - Assignment, 3.0 credits, grading scale: P, F
  • TEN1 - Examination, 4.5 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Written examination.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

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Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

Computer Science and Engineering

Education cycle

Second cycle

Add-on studies

No information inserted


Matskin, Mihhail

Supplementary information

In this course, the EECS code of honor applies, see: