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.
Person in charge
Antonio Moreno Ribas (
David Isern (
Generic Technical Competences
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
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.
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.
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.
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.
Acquisition of the basic theoretical concepts in the field of intelligent agents and multi-agent systems.
Design and implementation of a multi-agent in a team to solve a complex problem.
Introduction to intelligent agents. Definition.
Architectures: reactive, deliberative, hybrid.
Properties: reasoning, learning, autonomy, proactivity, etc.
Tipology: interface agents, information agents, heterogeneous systems.
Introduction to distributed intelligent systems. Communication. Standards. Coordination. Negotiation. Distributed planning. Voting. Auctions. Coalition formation. Application of multi-agent systems to industrial problems.
Practical exercise (in teams) in which a multi-agent system must be developed. Objectives:2 Week:
Examen of the theoretical content of the course Objectives:1 Week:
15 (Outside class hours) Type:
Theoretical lectures covering the content of the course
The teaching methodologies employed in this course are:
- Participative sessions.
- Supervision of practice sessions in the lab.
- Supervision and orientation in team work.
- Orientation of autonomous work.
- Personalised tutoring.
- Doubts sessions.
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.