Course detail

Agents and Multiagent Systems

FIT-AGSAcad. year: 2026/2027

The course provides a comprehensive overview of the theory, architectures, and practical implementation of autonomous agents and multi-agent systems. It bridges formal mathematical foundations, such as modal and temporal logics or BDI logic, with advanced agent-oriented software engineering. Students will explore reactive and mental architectures, the Jason development environment using AgentSpeak, FIPA standards for agent communication, and design methodologies like GAIA and Prometheus.

Furthermore, the course covers microeconomic and social aspects of agent interaction, including Rubinstein bargaining, game theory, formal argumentation, coalitional games, and distributed planning. To reflect contemporary paradigms, the practical component features platforms such as JADE and SPADE in Python, highlighting the integration of Large Language Models for reasoning, tool calling, and agent control. The course concludes with an introduction to Multi-Agent Reinforcement Learning.

Language of instruction

Czech

Number of ECTS credits

5

Assignment to study programme types

Master's

Mode of study

Not applicable.

Guarantor

doc. Ing. František Zbořil, Ph.D.

Department

Department of Intelligent Systems (UITS)

Entry knowledge

It is necessary to have fundamental knowledge of formal logic, artificial intelligence, system modelling and programming for this course.

Rules for evaluation and completion of the course

  • Mid-Term test
  • Team project
  • In order to obtain points in the exam, the exam must be prepared in such a way that it is scored higher than the minimum threshold indicated in the information system (25 points). Otherwise, the exam will be scored 0 points.

Aims

The objective of the course is to introduce students to the operational principles and design of agent-based systems featuring autonomous intelligent units  as well as multi-agent systems composed of multiple interacting agents. Students will learn to design, model, and program these systems and their individual components using both declarative and asynchronous modern software frameworks.

Graduates will acquire comprehensive theoretical and practical knowledge of contemporary approaches to building autonomous intelligent entities capable of operating in highly dynamic environments. They will understand the formal foundations of agent architectures, the BDI model, and FIPA standards for agent communication. Furthermore, students will gain deep insight into multi-agent problem-solving, including negotiation, conflict resolution, coalition formation, and distributed planning.

Practical lab sessions provide hands-on experience in implementing intelligent agents and multi-agent systems using Jason, JADE, and SPADE in Python. Students will also explore the integration of classical agent architectures with Large Language Models (LLMs) and the fundamentals of Multi-Agent Reinforcement Learning.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Russel, S.Norvig, P.: Artificial Intelligence, a Modern Approach, Pearson Education Inc., 2003, ISBN 0-13-080302-2 
Wooldridge, M.: Reasoning about Rational Agents, 2000, The MIT Press, Cambridge, MA, ISBN 0-262-23213-8
Woolridge, M.: Introduction to Multiagent Systems, Wiley, 2009 (EN)

Recommended reading

Russel, S., Norvig, P.: Artificial Intelligence, a Modern Approach, Pearson Education Inc., 2003, ISBN 0-13-080302-2
Shoham, Y, Leyton-Brown, K.: Multiagent systems, Algorithmic, Game-Theoretic, and Logical Foundations,  Cambridge University Press, 2009
Shoham, Y, Leyton-Brown, K.: Multiagent systems, Algorithmic, Game-Theoretic, and Logical Foundations,  Cambridge University Press, 2009

Wooldridge, M.: Reasoning about Rational Agents, 2000, The MIT Press, Cambridge, MA, ISBN 0-262-23213-8

Classification of course in study plans

  • Programme MITAI Master's

    specialization NHPC , 0 year of study, winter semester, elective
    specialization NVER , 0 year of study, winter semester, elective
    specialization NIDE , 0 year of study, winter semester, elective
    specialization NISY , 1 year of study, winter semester, compulsory, profile core courses
    specialization NEMB , 0 year of study, winter semester, elective
    specialization NSPE , 0 year of study, winter semester, elective
    specialization NEMB , 0 year of study, winter semester, elective
    specialization NBIO , 0 year of study, winter semester, elective
    specialization NSEN , 0 year of study, winter semester, elective
    specialization NVIZ , 0 year of study, winter semester, elective
    specialization NGRI , 0 year of study, winter semester, elective
    specialization NADE , 0 year of study, winter semester, elective
    specialization NISD , 0 year of study, winter semester, elective
    specialization NMAT , 0 year of study, winter semester, elective
    specialization NSEC , 0 year of study, winter semester, elective
    specialization NNET , 0 year of study, winter semester, elective
    specialization NMAL , 0 year of study, winter semester, elective
    specialization NCPS , 0 year of study, winter semester, elective

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

doc. Ing. František Zbořil, Ph.D.

Syllabus

  1. Introduction to Distributed Artificial Intelligence: Concept of an agent and multi-agent systems (MAS), environmental properties, agent typologies, and classifications.

  2. Formal Agent Models and Reactive Architectures: Wooldridge’s formal model of agents and environments, perceptions, states, and behavior functions (demonstrated on TileWorld). Reactive approach, situated automata, subsumption architecture, and layered architectures.

  3. Formal Logics in Agent Systems: Modal logics, possible-world semantics, Kripke structures, temporal logics (LTL and CTL), formal foundations of BDI logic.

  4. Mental States and BDI Architectures: Rational agents, intent-driven systems and their modeling, IRMA architecture, AgentSpeak framework.

  5. Advanced BDI Approaches: Extensions of the BDI model, intent synergies, late variable binding during plan execution to increase reasoning flexibility.

  6. BDI Programming in Jason: Practical aspects of BDI agents, environment interfaces, and architecture of the Jason system; demonstrations and code walkthroughs in AgentSpeak(L).

  7. FIPA Standards, Communication, and MAS Engineering: Abstract architecture and agent lifecycle, KQML and FIPA ACL languages, interaction protocols. GAIA and Prometheus design methodologies, agent roles in MAS.

  8. MAS Implementation Platforms and LLM Agents: Principles and architecture of JADE, transitioning to Python via the SPADE framework. Utilizing Large Language Models (LLMs) for agent reasoning, tool calling, Model Context Protocol (MCP), and LLM integration into SPADE.

  9. Negotiation, Voting, and Argumentation in MAS: Physical and mental conflicts, game theory, Rubinstein’s bargaining model, voting mechanisms, and formal argumentation.

  10. Coalitions and Coalitional Games: Theory of coalitional games, solution concepts (Core, Shapley value), and algorithms for coalitional structure generation (DP, Anytime IP).

  11. Coordinated Action and Distributed Planning: Centralized vs. distributed planning, intent coordination methods, and Distributed Constraint Satisfaction/Optimization Problems (DisCSP/DCOP).

  12. Social Behavior in MAS: Principles of agent societies, concepts of commitment, norms, and social structures.

  13. Learning in Agent Systems: Reinforcement Learning (RL) and Multi-Agent Reinforcement Learning (MARL) for agent behavior.

Exercise in computer lab

13 hours, compulsory

Teacher / Lecturer

doc. Ing. František Zbořil, Ph.D.

Syllabus

  1. Two practices - JASON system
  2. Two practices - JADE system
  3. Two practices - JACK system

Project

13 hours, compulsory

Teacher / Lecturer

Ing. Radek Kočí, Ph.D.

Syllabus

Team project - design of a mulitagent system, cooperative planning, coordination, negotiation