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Multi-agent Systems

Public syllabus for 2025-2026

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Academic overview

Programme
CS
Period
Year 1, Semester 2
Credits
5
Weeks
14

Curriculum placement

Appears in study plans

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

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
10
Individual Study Bibliography study Field study Homework Tutoring Others
73 38 14 14 7 0
Overall
125

Learning outcomes

Knowledge

  • Study of multi-agent models and architectures
  • Knowledge about the design of MAS applications
  • (C1) Understanding of statistical analysis methods and communicating results respecting statistical validation standards
  • (C2) Understanding the principles of processing Big Data with the provision of fast access, elements of consistency, security and confidentiality

Skills

  • Developing applications based on multi-agent models
  • (A3) Designing, implementing and testing software modules suitable for processing and analyzing large volumes of data
  • (A1) Identification of techniques and IT tools suitable for data processing and building models
  • Ability to analyse and design MAS applications
  • (A2) Installation, configuration and use of platforms for processing large volumes of data
  • 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

Responsibility

  • (6a03a0952355ae3a04d2f2fe) Maintaining autonomy, integrity and independence in professional opinions

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

1.Michael Wooldridge - An Introduction to Multi - Agent Systems, John Wiley & Sons, 20092.F. Bellifemine, G. Claire, D. Greenwood – Developing Multi-Agent Systems with Jade, John Wiley \& Sons' 20073.S.Russel, P. Norvig - Artificial Intelligence. A Modern Approach, second edition, Prentice Hall, 20104.J. Ferber - Les systemes multi-agents. Vers une intelligence collective, InterEditions, 19955.M. dInverno - Understanding Agent Systems, Springer Verlag, second edition, 20046.M. Singh and M. Huhns. Readings in Agents. Morgan-Kaufmann Publishers, 1997.7.M. P. Singh - Multiagent Systems - A theoretical Framework for Intentions, Know-How, and Communications, Springer Verlag, 19948.J. M. Bradshaw - Software agents, MIT Press, 19979.G. Weiss, eds. Multi-Agent Systems. A modern approach to Distributed AI, The MIT Press, 1999.10.G. F. Luger, W. A. Stubblefield - Artificial intelligence and the design of expert systems, Benjammin/Cummings Pbs., 200511.T. Ishida - Parallel, Distributed and Multiagent Production Systems, Springer Verlag, 199412.R. Engelmore, T. Morgan - Blackboard systems, Addison Wesley, 198813.H. Kitano, J. A. Hendler - Massively Parallel Artificial Intelligence,14.MIT Press, 199415.M. Watson - Intelligent Java applications for the Internet and intranets, Morgan Kaufmann, 1997 (sau versiunea in romana, ed. ALL, 1999)16.M. Wooldridge, N. R. Jennings - Intelligent agents: Theory and practice, Knowledge engineering review, 199517.*** IEEE - Intelligent systems18.*** Autonomous Agents and Multi-Agent Systems, Kluwer Academic Pbs.19.J. Giarratano, G. Riley - Expert Systems: Principles and Programming, PWS Pbs. Comp., ITP, 4th edition, 200520.Ernest Friedman-Hill - Jess in action. Java rule-based systems, Manning Publ. Co., 200321.http://www.ghg.net/clips/Version623.html22.http://herzberg.ca.sandia.gov/jess/23.http://myri1.ieat.ro/mas/; http://www.cougaar.org/24.http://www.ai.sri.com/oaa/; 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:1.Michael Wooldridge - An Introduction to Multi - Agent Systems, John Wiley & Sons, 20092.F. Bellifemine, G. Claire, D. Greenwood – Developing Multi-Agent Systems with Jade, John Wiley \& Sons' 20073.S.Russel, P. Norvig - Artificial Intelligence. A Modern Approach, second edition, Prentice Hall, 20104.J. Ferber - Les systemes multi-agents. Vers une intelligence collective, InterEditions, 19955.M. dInverno - Understanding Agent Systems, Springer Verlag, second edition, 20046.M. Singh and M. Huhns. Readings in Agents. Morgan-Kaufmann Publishers, 1997.7.M. P. Singh - Multiagent Systems - A theoretical Framework for Intentions, Know-How, and Communications, Springer Verlag, 19948.J. M. Bradshaw - Software agents, MIT Press, 19979.G. Weiss, eds. Multi-Agent Systems. A modern approach to Distributed AI, The MIT Press, 1999.10.G. F. Luger, W. A. Stubblefield - Artificial intelligence and the design of expert systems, Benjammin/Cummings Pbs., 200511.T. Ishida - Parallel, Distributed and Multiagent Production Systems, Springer Verlag, 199412.R. Engelmore, T. Morgan - Blackboard systems, Addison Wesley, 198813.H. Kitano, J. A. Hendler - Massively Parallel Artificial Intelligence,14.MIT Press, 199415.M. Watson - Intelligent Java applications for the Internet and intranets, Morgan Kaufmann, 1997 (sau versiunea in romana, ed. ALL, 1999)16.M. Wooldridge, N. R. Jennings - Intelligent agents: Theory and practice, Knowledge engineering review, 199517.*** IEEE - Intelligent systems18.*** Autonomous Agents and Multi-Agent Systems, Kluwer Academic Pbs.19.J. Giarratano, G. Riley - Expert Systems: Principles and Programming, PWS Pbs. Comp., ITP, 4th edition, 200520.Ernest Friedman-Hill - Jess in action. Java rule-based systems, Manning Publ. Co., 200321.http://www.ghg.net/clips/Version623.html22.http://herzberg.ca.sandia.gov/jess/23.http://myri1.ieat.ro/mas/; http://www.cougaar.org/24.http://www.ai.sri.com/oaa/; 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

Written examination / Examinare scrisă: •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)