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Multi-agent Systems

Public syllabus for 2025-2026

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Academic overview

Programme
AIDC
Period
Year 1, Semester 2
Credits
6
Weeks
14

Curriculum placement

Appears in study plans

AIDC (2025 - 2027) Year 1, Semester 2 Elective subject, group 1

Teaching team

Course coordinator
Iuhasz G
Seminar coordinators
Iuhasz G

Learning time distribution

Total
Curriculum Lecture Practice Total Weekly Lecture Practice
42 28 14 3 2 1
Exam hours
4
Individual Study Bibliography study Field study Homework Tutoring Others
104 40 20 40 4 0
Overall
150

Learning outcomes

Knowledge

  • Knowledge about the design of MAS applications
  • (C1) Knowledge, understanding and use in a practical context of concepts of probabilistic modeling (probability distributions, Markov models), statistics (descriptive analysis, inference techniques, statistical tests, regression) and linear and nonlinear optimization techniques
  • (C2) Knowledge of statistical methods specific to different types of processing and understanding of how machine learning algorithms can be used

Skills

  • (A3) Designing, implementing and testing software modules suitable for processing and analyzing large volumes of data
  • Ability to identify complex problems solving methods
  • Ability to analyse and design MAS applications
  • Ability to implement and validate MAS applications
  • Ability to approach a data processing task, to identify the architecture and the appropriate algorithm
  • (A4) Using distributed parallel processing principles in designing scalable applications
  • (A2) Identifying statistical and machine learning techniques as well as appropriate IT tools for data processing and building decision models

Responsibility

  • (R1) Responsibility to act in accordance with the interest of users

Online platform

Google Classroom

Course content

Content Methods Obs
C1-2. Intelligent systems University lecture, conversation, example Bibliography presentation, and slides
C3. Distributed problem solving University lecture, conversation, example Including real life examples of distributed problem solving.
C4-5. Parallel algorithms in AI (Knowledge representation; Rules compilation; Reasoning). University lecture, conversation, example
C6. Agent based systems University lecture, conversation, example Presenting Reactive, Deliberative and Hybrid models
C7-9. Blackboard model (Distributed expert systems; Cooperation models; Classification of blackboard systems; Applications. University lecture, conversation, example Including Real life examples of blackboard systems.
C10-14. Multi-agent models (Foundations; Agents classification; Interaction and cooperation; Communication; Collaboration and coordination, Mobile agents, Security) University lecture, conversation, example Including modern agent based systems such as Serf and Consul from Hashicorp.

Course bibliography

Michael Wooldridge - An Introduction to Multi - Agent Systems, John Wiley & Sons, 2009 F. Bellifemine, G. Claire, D. Greenwood – Developing Multi-Agent Systems with Jade, John Wiley \& Sons' 2007 S.Russel, P. Norvig - Artificial Intelligence. A Modern Approach, second edition, Prentice Hall, 2010 J. Ferber - Les systemes multi-agents. Vers une intelligence collective, InterEditions, 1995 M. dInverno - Understanding Agent Systems, Springer Verlag, second edition, 2004 M. Singh and M. Huhns. Readings in Agents. Morgan-Kaufmann Publishers, 1997. M. P. Singh - Multiagent Systems - A theoretical Framework for Intentions, Know-How, and Communications, Springer Verlag, 1994 J. M. Bradshaw - Software agents, MIT Press, 1997G. Weiss, eds. Multi-Agent Systems. A modern approach to Distributed AI, The MIT Press, 1999. G. F. Luger, W. A. Stubblefield - Artificial intelligence and the design of expert systems, Benjammin/Cummings Pbs., 2005 T. Ishida - Parallel, Distributed and Multiagent Production Systems, Springer Verlag, 1994 R. Engelmore, T. Morgan - Blackboard systems, Addison Wesley, 1988 H. Kitano, J. A. Hendler - Massively Parallel Artificial Intelligence,MIT Press, 1994 M. Watson - Intelligent Java applications for the Internet and intranets, Morgan Kaufmann, 1997 (sau versiunea in romana, ed. ALL, 1999) M. Wooldridge, N. R. Jennings - Intelligent agents: Theory and practice, Knowledge engineering review, 1995*** IEEE - Intelligent systems*** Autonomous Agents and Multi-Agent Systems, Kluwer Academic Pbs .J. Giarratano, G. Riley - Expert Systems: Principles and Programming, PWS Pbs. Comp., ITP, 4th edition, 2005 Ernest Friedman-Hill - Jess in action. Java rule-based systems, Manning Publ. Co., 2003 https://www.clipsrules.net/ https://pade.readthedocs.io/en/latest/ https://spade-mas.readthedocs.io/en/latest/readme.html http://jade.tilab.com/

Seminar content

Content Methods Obs
Parallel algorithms and architectures for rule based systems Expert systems / MAS developed on: Clips, JADE, PADE, SPADE, Spade, LangGraph, Mesa, uAgent etc. As well as application specific distributed agent based software such as Serf and Consul.
Bibliography: Michael Wooldridge - An Introduction to Multi - Agent Systems, John Wiley & Sons, 2009F. Bellifemine, G. Claire, D. Greenwood – Developing Multi-Agent Systems with Jade, John Wiley \& Sons' 2007S.Russel, P. Norvig - Artificial Intelligence. A Modern Approach, second edition, Prentice Hall, 2010J. Ferber - Les systemes multi-agents. Vers une intelligence collective, InterEditions, 1995M. dInverno - Understanding Agent Systems, Springer Verlag, second edition, 2004M. Singh and M. Huhns. Readings in Agents. Morgan-Kaufmann Publishers, 1997.M. P. Singh - Multiagent Systems - A theoretical Framework for Intentions, Know-How, and Communications, Springer Verlag, 1994J. M. Bradshaw - Software agents, MIT Press, 1997G. Weiss, eds. Multi-Agent Systems. A modern approach to Distributed AI, The MIT Press, 1999.G. F. Luger, W. A. Stubblefield - Artificial intelligence and the design of expert systems, Benjammin/Cummings Pbs., 2005T. Ishida - Parallel, Distributed and Multiagent Production Systems, Springer Verlag, 1994R. Engelmore, T. Morgan - Blackboard systems, Addison Wesley, 1988H. Kitano, J. A. Hendler - Massively Parallel Artificial Intelligence,MIT Press, 1994M. Watson - Intelligent Java applications for the Internet and intranets, Morgan Kaufmann, 1997 (sau versiunea in romana, ed. ALL, 1999)M. Wooldridge, N. R. Jennings - Intelligent agents: Theory and practice, Knowledge engineering review, 1995*** IEEE - Intelligent systems*** Autonomous Agents and Multi-Agent Systems, Kluwer Academic Pbs.J. Giarratano, G. Riley - Expert Systems: Principles and Programming, PWS Pbs. Comp., ITP, 4th edition, 2005Ernest Friedman-Hill - Jess in action. Java rule-based systems, Manning Publ. Co., 2003https://www.clipsrules.net/ https://pade.readthedocs.io/en/latest/ https://spade-mas.readthedocs.io/en/latest/readme.html http://jade.tilab.com/

Seminar bibliography

(none)

Corroboration

(none)

AI tools guidance

The use of Generative AI technologies is permitted for research purposes, code debugging, and minimal generations. All work which uses generative AI should have a signed disclaimer where students describe the exact use cases they used such tools in their project/homework. This disclaimer should also contain the steps taken to validate and double-check the generative AI outputs.

Evaluation and delivery

Activity Criteria Methods Percentage
C
  • Theoretical and practice knowledge evaluation
  • Write exam / Project / Report
  • 50.0%
C
  • Periodic evaluation
  • Tests, Home work
  • 20.0%
S
  • Labs and homework evaluation
  • Computer tests;Home work
  • 30.0%

Performance standards

Course: The capacity to understand basic concepts of MAS and the capacity to understand basic principles to implement MAS. Lab.: Middle level MAS problem solving

Additional info

(none)