Introduction to Multiagent Systems

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Credits
5
Types
Compulsory
Requirements
This subject has not requirements, but it has got previous capacities
Department
CS;URV
Web
https://campusvirtual.urv.cat
This course provides the basic theoretical knowledge about intelligent agents and multi-agent systems. The first part of the course covers the different types of agents, their properties and architectures. The second part includes a thorough description of several coordination methods in multi-agent systems.

The course also includes a practical component on the lab, in which students have to work in teams to develop a multi-agent system.

Teachers

Person in charge

  • Jordi Pascual Fontanilles ( )

Weekly hours

Theory
2
Problems
0
Laboratory
1
Guided learning
0
Autonomous learning
5.33

Competences

Generic Technical Competences

Generic

  • CG3 - Capacity for modeling, calculation, simulation, development and implementation in technology and company engineering centers, particularly in research, development and innovation in all areas related to Artificial Intelligence.

Technical Competences of each Specialization

Academic

  • CEA1 - Capability to understand the basic principles of the Multiagent Systems operation main techniques , and to know how to use them in the environment of an intelligent service or system.
  • CEA8 - Capability to research in new techniques, methodologies, architectures, services or systems in the area of ??Artificial Intelligence.

Professional

  • CEP3 - Capacity for applying Artificial Intelligence techniques in technological and industrial environments to improve quality and productivity.
  • CEP4 - Capability to design, write and report about computer science projects in the specific area of ??Artificial Intelligence.

Transversal Competences

Teamwork

  • CT3 - Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available resources.

Information literacy

  • CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.

Analisis y sintesis

  • CT7 - Capability to analyze and solve complex technical problems.

Objectives

  1. Acquisition of the basic theoretical concepts in the field of intelligent agents and multi-agent systems.
    Related competences: CEA8, CEA1, CT4,
  2. Design and implementation of a multi-agent in a team to solve a complex problem.
    Related competences: CG3, CEP3, CEP4, CT3, CT7,

Contents

  1. Intelligent Agents
    Introduction to intelligent agents. Definition.
    Architectures: reactive, deliberative, hybrid.
    Properties: reasoning, learning, autonomy, proactivity, etc.
    Tipology: interface agents, information agents, heterogeneous systems.
  2. Multi-Agent Systems
    Introduction to distributed intelligent systems. Communication. Standards. Coordination. Negotiation. Distributed planning. Voting. Auctions. Coalition formation. Application of multi-agent systems to industrial problems.

Activities

Activity Evaluation act


Practical exercise

Practical exercise (in teams) in which a multi-agent system must be developed.
Objectives: 2
Week: 15
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
40h

Theoretical exam

Examen of the theoretical content of the course
Objectives: 1
Week: 15
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
39.9h

Lectures

Theoretical lectures covering the content of the course
  • Theory: Lectures
Objectives: 1
Contents:
Theory
30h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Lab sessions

Work sessions in the computer lab
  • Laboratory: Practical sessions in the computer lab
Objectives: 2
Contents:
Theory
0h
Problems
0h
Laboratory
15h
Guided learning
0h
Autonomous learning
0h

Teaching methodology

The teaching methodologies employed in this course are:
- Lectures.
- Participative sessions.
- Supervision of practice sessions in the lab.
- Supervision and orientation in team work.
- Orientation of autonomous work.
- Personalised tutoring.
- Doubts sessions.

Evaluation methodology

Final exam: 40%
Practical exercise, developed in teams: 60%. This exercise will include the analysis of the architectures and types of agents appropriate for the exercise (10%), an analysis of the most adequate coordination and negotiation mechanisms (20%) and a final oral and written presentation of the complete multi-agent system (30%). It is necessary to complete the practical exercise to pass the course.

Bibliography

Basic:

Complementary:

Web links

Previous capacities

Knowledge of basic Artificial Intelligence concepts.
Good programming skills in Java.