Course details

Agents and Multiagent Systems

AGS Acad. year 2022/2023 Winter semester 5 credits

Current academic year

Concepts of artificial agent and multiagent systems, reactive and rational agents. The basic architectures of agent systems, layered architecture, subsumptional architecture. Agent's mental states, intentional systems and their models. BDI system architectures. Communication in multiagent systems, KQML and ACL languages, the basic interaction protocols. Physical and mental conflicts, general approaches to conflict solving, voting, negotiation and argumentation. Behavior coordination and methods for distributed planning. Social aspects in MAS, obligations and norms. FIPA abstract platform, agent's life cycle. Development and realization of multiagent systems, GAIA methodology and JADE implementation tool.

Guarantor

Zbořil František, doc. Ing., Ph.D. (DITS)

Course coordinator

Zbořil František, doc. Ing., CSc. (DITS)

Language of instruction

Czech

Completion

Examination

Time span

  • 26 hrs lectures
  • 13 hrs pc labs
  • 13 hrs projects

Assessment points

  • 60 pts final exam (written part)
  • 20 pts mid-term test (written part)
  • 20 pts projects

Department

Department of Intelligent Systems (UITS)

Lecturer

Zbořil František, doc. Ing., Ph.D. (DITS)

Instructor

Zbořil František, doc. Ing., Ph.D. (DITS)

Course Web Pages

Subject specific learning outcomes and competences

Course graduate gains knowledge about recent approaches to development of multiagent systems. It comprises agents' architectures, interagent communication languages and protocol, as well as multiagent organizations.
Programming of agent systems and heterogeneous systems with agents, creation of intelligent systems using multiagent methodology and resolving conflicts with these methods

Learning objectives

The aim of this course is to acquaint students with principles of operations and with designs of systems with agents - autonomous intelligent entities and also with systems containing more such agents. Also to learn how to create such systems and how to programming particular elements there.

Why is the course taught

 A student gains knowledge how to develop reactive and flexible intelligent systems, furthermore she or he learns how to realize systems with a population of intelligent agents which can interact correctly. These abilities are useful for development of webs of services, autonomous control systems or Industry-4 robotic systems.

Prerequisite knowledge and skills

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

Study 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
  • 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

Fundamental 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

Syllabus of lectures

  1. Introduction to distributed artificial intelligence. Concepts of agent, environment, agent classification.
  2. Fundamental architectures of reactive and deliberative agents. Situated automata, Subsumptional architecture.
  3. Formal approaches to the agent systems. Modal logics, Epistemic, Temporal, CTL and BDI logics.
  4. Rational agent, agent's mental states, IRMA, AgentSpeak(L), dMARS architectures.
  5. Agent Oriented Programming (AOP), system Agent-0
  6. Agent's programming in JASON
  7. Multiagent systems (MAS), general principles of cooperation and conflict solving, game theory for multiagent systems.
  8. Communication in MAS, KQML and ACL languages, interaction protocols.
  9. Negotiation, argumentation, voting. Algorithms, protocols and examples. 
  10. FIPA abstract architecture. Programming in JADE
  11. Collaborative planning, mutual decisioning.
  12. MAS modelling. Agent's roles, AUML, GAIA, Prometheus.
  13. Realization of MAS for small devices, mobile agents and their security.

 

Syllabus of computer exercises

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

Syllabus - others, projects and individual work of students

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

Progress assessment

  • Mid-Term test
  • Team project

Controlled instruction

Mid-term exam

Exam prerequisites

At least 1 point during semester

Course inclusion in study plans

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