Multi-agent Technologies

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  • English

    course language

  • 6 weeks

    course duration

  • от 4 до 6 часов в неделю

    needed to educate

  • 2 credit points

    for credit at your university

Dear learners, please note that only part of the materials is available for free. All course materials will be available after certification payment.



A significant reduction in the size of computing devices and an increase in their speed provide new opportunities for solving traditionally complex, multidimensional problems of optimization, resource allocation, logistics, etc. Multi-agent technologies (MT) were born at the junction of modern distributed methods for solving various problems, parallel programming and artificial intelligence.

At the heart of MT is a decentralized approach to solving problems, in which dynamically updated information in a distributed network of intelligent agents is processed not in some center, but directly at the agents along with locally available information from neighbors. At the same time, both the resource and time costs for communication in the network and the time for processing and decision-making in the center of the entire system (if there is one) are significantly reduced.
Emergent intelligence (intellectual resonance, swarm intelligence) is an occurrence of unexpected properties that a system possesses, but none of its individual elements has.
Key feature is the dynamics and unpredictability of the decision making process.


The course is taught completely online.


This course may be of interest to bachelors, masters and graduate students of higher educational institutions, as well as to all those who are interested in the designated topic. 

Course program

  1. Introduction
  2. Emergent Intelligence
  3. Multiagent Systems
  4. Development of Multi-agent Systems
  5. Multiagent Control
  6. Load Balancing

Education results

Upon completing the course on "Multi-agent Technologies," learners will:

  1. Gain insights into the origins and development of multi-agent technologies.

  2. Understand the role of MT in addressing complex, multidimensional problems in optimization, resource allocation, logistics, and more.

  3. Master the concept of decentralized problem-solving in MT.

  4. Learn how dynamically updated information is processed within a distributed network of intelligent agents.

  5. Explore the benefits of MT in reducing resource and time costs for communication in a network.

  6. Understand how processing and decision-making are distributed across agents, minimizing centralization.

  7. Grasp the concept of emergent intelligence and how it manifests in multi-agent systems

Formed competencies

The course is aimed at the formation of general learning competencies for Bachelor’s and Specialists’ programs, as well as other competencies included in the education program.

Education directions

Граничин Олег Николаевич

Position: профессор кафедры системного программирования математико-механического факультета СПбГУ

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