Program Overview

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University Programs

The university offers a range of programs for students, including bachelor's degrees, master's degrees, and integrated bachelor-master degrees.

Bachelor's Degrees

• Bachelor Degree in Informatics Engineering
  • Enrolment: Available places
  • Curriculum: Syllabus, Reassessment, Specializations, Competences, Competences for degree subjects
  • Faculty
  • Bachelor's Thesis
  • Timetables
  • Exams
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• Bachelor Degree in Data Science and Engineering
  • Enrolment: Available places
  • Curriculum: Syllabus, Competences, Competences for degree subjects
  • Faculty
  • Timetables
  • Exams
  • Academic Regulations and organization
• Bachelor Degree in Artificial Intelligence
  • Enrolment: Places lliures
  • Curriculum: Competences, Syllabus, Competences for degree subjects
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  • Exams
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  • Bachelor's thesis
• Bachelor Degree in Bioinformatics
  • Enrolment: Available places
  • Curriculum: Learning Outcomes, Syllabus
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  • Exams
  • Academic Regulations
• Integrated Bachelor Master Degree
  • Enrolment
  • Curriculum

Master's Degrees

• Master in Informatics Engineering
  • Enrolment: Available places
  • Curriculum: Syllabus, Competences, Competences for degree subjects
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  • Master's Thesis
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• Master in Informatics Engineering - Industrial Modality
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• Master in Innovation and Research in Informatics
  • Enrolment: Available places
  • Curriculum: Syllabus, Specializations, Competences, Competences for degree subjects
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  • Master's Thesis
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• Master in Artificial Intelligence
  • Enrolment: Available places
  • Curriculum: Syllabus, Competences, Competences for degree subjects
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  • Master's Thesis
  • Timetables
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• Master in Cybersecurity
• Master in Data Science
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  • Curriculum: Syllabus, Competences, Competences for degree subjects
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  • Exams
  • Master's Thesis
    • Gender Competency
• Erasmus Mundus Master in Big Data Management and Analytics
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• Master in Urban Mobility
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• EUMaster4HPC
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• Other Masters
  • Master in Pure and Applied Logic
  • Master in Computational Modelling in Physics, Chemistry and Biochemistry

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Introduction to Multiagent Systems

Course Description

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.

Credits and Type

• Credits: 5
• Type: Compulsory

Requirements

This subject has no requirements but has previous capacities.

Department

CS;URV

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

• 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 specialization, and for critically assessing the results of this management.

Analysis and Synthesis

• 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.
2. Design and implementation of a multi-agent in a team to solve a complex problem.

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

• Practical exercise (in teams) in which a multi-agent system must be developed.
• Theoretical exam
• Lectures
• Lab sessions

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, an analysis of the most adequate coordination and negotiation mechanisms, and a final oral and written presentation of the complete multi-agent system. It is necessary to complete the practical exercise to pass the course.

Bibliography

Basic

• An introduction to multiagent systems - Wooldridge, M.J, John Wiley & Sons, 2009.

Complementary

• Agent technology for e-commerce - Fasli, M, John Wiley & Sons, 2007.
• Agentes software y sistemas multi-agente : conceptos, arquitecturas y aplicaciones - Mas, A, Prentice-Hall, 2005.

Web Links

• Moodle space of the course at URV.

Previous Capacities

• Knowledge of basic Artificial Intelligence concepts.
• Programming skills in Python.