Module also offered within study programmes:
General information:
Name:
Group artificial intelligence
Course of study:
2019/2020
Code:
RIME-2-208-WM-s
Faculty of:
Mechanical Engineering and Robotics
Study level:
Second-cycle studies
Specialty:
Wytwarzanie mechatroniczne
Field of study:
Mechatronic Engineering
Semester:
2
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Full-time studies
Responsible teacher:
prof. dr hab. inż. Dobrowolski Grzegorz (grzela@agh.edu.pl)
Module summary

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Social competence: is able to
M_K001 From the perspective of multi-agent systems students can deeply understand some relationships among members of a human group. Participation in a discussion,
Activity during classes,
Involvement in teamwork
Skills: he can
M_U001 Students can build a simple-function multi-agent system using one of specialized development environments. Execution of laboratory classes
Knowledge: he knows and understands
M_W001 Students gain knowledge of a class of information systems which is characterized by decentralization stemmed from autonomy of their elements – agents Participation in a discussion,
Oral answer
M_W002 Students acquire a base for designing and implementing the systems under consideration. Participation in a discussion,
Oral answer
Number of hours for each form of classes:
Sum (hours)
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
56 28 0 28 0 0 0 0 0 0 0 0
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
Social competence
M_K001 From the perspective of multi-agent systems students can deeply understand some relationships among members of a human group. + - + - - - - - - - -
Skills
M_U001 Students can build a simple-function multi-agent system using one of specialized development environments. - - + - - - - - - - -
Knowledge
M_W001 Students gain knowledge of a class of information systems which is characterized by decentralization stemmed from autonomy of their elements – agents + - - - - - - - - - -
M_W002 Students acquire a base for designing and implementing the systems under consideration. + - - - - - - - - - -
Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 150 h
Module ECTS credits 6 ECTS
Udział w zajęciach dydaktycznych/praktyka 56 h
Preparation for classes 54 h
Realization of independently performed tasks 40 h
Module content
Lectures (28h):

  1. Introduction to group artificial intelligence: subject, origin, classification of agents and their systems, applications.
  2. Basic notions and definitions: agent, multi-agent system, elementary features of a multi-agent system.
  3. The vicinity and environment of a multi-agent system, its spatial structure, resources.
  4. The agent’s rationality: action selection mechanism, reactive agent, agent with the strategy (cognitive). The system goal (strategy), the notion of system integrity (functional and resource-based).
  5. Communicating agents. Aspects of the communication process: transportation layer, interaction protocol, implementation of a protocol in the system, ontology. The systems of open communication – KQML: syntax and semantics, some protocols; FIPA. Interoperability and ontologies.
  6. Organizing and self-organizing agents: negotiation, auction, appropriate protocols. Information agents as realization of interface with the environment.
  7. Elements of specification of systems of software agents: decomposition as an analytic tool for multi-agent systems, specification of tasks of Level I (system) and II (agent).
  8. Specification of the agent’s kernel– ASM.
  9. Main rules of the agent’s functioning: symbolic inference, algorithmic reaction to environmental influence, the mixed rule.

Laboratory classes (28h):

  1. Programming with JADE Platform – Basics
  2. Programming with JADE Platform – Communication, Ontologies
  3. Introduction to programming with JADEX
  4. Introduction to programming with MadKit

Additional information
Teaching methods and techniques:
  • Lectures: Treści prezentowane na wykładzie są przekazywane w formie prezentacji multimedialnej w połączeniu z klasycznym wykładem tablicowym wzbogaconymi o pokazy odnoszące się do prezentowanych zagadnień.
  • Laboratory classes: W trakcie zajęć laboratoryjnych studenci samodzielnie rozwiązują zadany problem praktyczny, dobierając odpowiednie narzędzia. Prowadzący stymuluje grupę do refleksji nad problemem, tak by otrzymane wyniki miały wysoką wartość merytoryczną.
Warunki i sposób zaliczenia poszczególnych form zajęć, w tym zasady zaliczeń poprawkowych, a także warunki dopuszczenia do egzaminu:

Participation rules in classes:
  • Lectures:
    – Attendance is mandatory: No
    – Participation rules in classes: Studenci uczestniczą w zajęciach poznając kolejne treści nauczania zgodnie z syllabusem przedmiotu. Studenci winni na bieżąco zadawać pytania i wyjaśniać wątpliwości. Rejestracja audiowizualna wykładu wymaga zgody prowadzącego.
  • Laboratory classes:
    – Attendance is mandatory: Yes
    – Participation rules in classes: Studenci wykonują ćwiczenia laboratoryjne zgodnie z materiałami udostępnionymi przez prowadzącego. Student jest zobowiązany do przygotowania się w przedmiocie wykonywanego ćwiczenia, co może zostać zweryfikowane kolokwium w formie ustnej lub pisemnej. Zaliczenie zajęć odbywa się na podstawie zaprezentowania rozwiązania postawionego problemu. Zaliczenie modułu jest możliwe po zaliczeniu wszystkich zajęć laboratoryjnych.
Method of calculating the final grade:

The average value is calculated from all grades obtained from lectures and laboratories:
The final grade is calculated as follows:

if sr>4.75 then OK:=5.0 else
if sr>4.25 then OK:=4.5 else
if sr>3.75 then OK:=4.0 else
if sr>3.25 then OK:=3.5 else OK:=3

Sposób i tryb wyrównywania zaległości powstałych wskutek nieobecności studenta na zajęciach:

Prerequisites and additional requirements:

Course of Methods of Artificial Intelligence

Recommended literature and teaching resources:
  1. Wooldridge M.J.: An Introduction to Multiagent Systems. John Wiley & Sons 2002
  2. Weiss G. (red.): Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. The MIT Press 1999
  3. F. Bellifemine, G. Caire, D. Greenwood. Developing Multi-Agent Systems with JADE. John Wiley & Sons, 2007
  4. R.H. Bordini, J.F. Hübner, M. Wooldridge. Programming Multi-Agent Systems in Agent-Speak using Jason. John Wiley & Sons, 2007
Scientific publications of module course instructors related to the topic of the module:

Additional scientific publications not specified

Additional information:

None