Module also offered within study programmes:
General information:
Code:
UBPJO-031
Name:
Group artificial intelligence
Profile of education:
Academic (A)
Lecture language:
English
Semester:
Spring, Fall
Responsible teacher:
Dobrowolski Grzegorz (grzela@agh.edu.pl)
Academic teachers:
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
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
M_U001 Students can build a simple-function multi-agent system using one of specialized development environments. Execution of laboratory classes
Knowledge
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
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
Others
E-learning
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. + - - - - - - - - - -
Module content
Lectures:

  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:

  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

Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 150 h
Module ECTS credits 6 ECTS
Participation in lectures 28 h
Participation in laboratory classes 28 h
Preparation for classes 54 h
Realization of independently performed tasks 40 h
Additional information
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

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