IDENTIFYING DATA 2016_17
Subject (*) MULTI-AGENT SYSTEMS Code 17685104
Study programme
Computer Security Engineering and Artificial Intelligence (2016)
 Cycle 2nd
Descriptors Credits Type Year Period
4.5 Compulsory First 1Q
Language
Anglès
Department Computer Engineering and Mathematics
Coordinator
MORENO RIBAS, ANTONIO
 E-mail
Lecturers
Web http://moodle.urv.cat
General description and relevant information Conceptes bàsics de l'àrea dels agents intel·ligents i els sistemes multi-agent.

Competences
Type A Code Competences Specific
 A1 Integrate the fundamental technology, applications, services and systems of Computer Security and Artificial Intelligence,in a broader, multidisciplinary context.
 A5 Analyse the information needs considered in an environment and execute all stages of the construction process of a secure information system.
 A9 Apply computational, mathematical, statistical and artificial intelligence methods in order to model, design and develop applications, services, smart systems and knowledge-based systems.
 G1 Project, calculate and design products, processes and installations in the areas of Computer Security and Artificial Intelligence
 G2 Perform mathematical modelling, calculation and simulation in company technology and engineering centres, particularly in tasks of research, development and innovation in the areas of Computer Security and Artificial Intelligence
Type B Code Competences Transversal
 CT2 Formular valoracions a partir de la gestió i ús eficient de la informació.
 CT3 Resoldre problemes complexes de manera crítica, creativa i innovadora en contextos multidisciplinars.
 CT4 Treballar en equips multidisciplinars i en contextos complexes.
 CT5 Comunicar idees complexes de manera efectiva a tot tipus d’audiències.
 CT7 Aplicar els principis ètics i de responsabilitat social com a ciutadà i com a professional.
Type C Code Competences Nuclear

Learning outcomes
Type A Code Learning outcomes
 A1 Analitza els problemes i les seves causes des d'un enfocament global i de mitjà i llarg termini.
Integra dispositius i / o mètodes computacionals en contextos diversos.
 A5 Identifica els components d'un problema de presa de decisions i saber decidir el tipus de model de presa de decisions més adequat.
 A9 Diferencia els diversos tipus d'agents intel·ligents i saber utilitzar cada un d'ells.
 G1 Integra els coneixements teòrics amb les realitats a les quals es poden aplicar.
 G2 Aplica les tècniques apreses en contextos concrets.
Type B Code Learning outcomes
 CT2 Formular valoracions a partir de la gestió i ús eficient de la informació.
 CT3 Resoldre problemes complexes de manera crítica, creativa i innovadora en contextos multidisciplinars.
 CT4 Treballar en equips multidisciplinars i en contextos complexes.
 CT5 Comunicar idees complexes de manera efectiva a tot tipus d’audiències.
 CT7 Aplicar els principis ètics i de responsabilitat social com a ciutadà i com a professional.
Type C Code Learning outcomes

Contents
Topic Sub-topic
Intelligent agents (6 hs) Intelligent agents. Definition. Properties. Characteristics. Tipology.
Multi-agent systems (24 hs) Introduction to distributed intelligent systems. Communication. Standards. Coordination. Negotiation. Distributed planning. Voting. Auctions. Coalition formation. Application of MAS to real problems.

Planning
Methodologies  ::  Tests
  Competences (*) Class hours
 Hours outside the classroom
 (**) Total hours
Introductory activities
1 1.5 2.5
Presentations / expositions
A5
G2
CT2
CT5
 1 1.5 2.5
Debates
A5
G2
CT2
 1 1.5 2.5
Material reading and studying
A9
 25 37.5 62.5
Assignments
A1
G1
CT3
CT4
CT7
 13 19.5 32.5
Personal tuition
1 0 1
 
Objective multiple-choice tests
A9
 3 6 9
 
(*) On e-learning, hours of virtual attendance of the teacher.
(**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies
  Description
Introductory activities Presentation of the topic, describing the contents, biblography, work mehodology, evaluation mechanism.
Presentations / expositions Presentation of the practical exercise at the end of the term.
Debates Discussions during the term on the practical exercise to be developed.
Material reading and studying Lectura i estudi dels continguts de l’assignatura.
Assignments Realització d’exercicis pràctics usant les TIC i guiades pel professor.
Personal tuition Personalised support to clarify the doubts on the theoretical concepts and to solve practical exercises with agent technology.

Personalized attention
Description
Personalised support through ICTs (mail, chat, videoconference) to clarify the doubts on the theoretical concepts and to solve practical exercises with agent technology.

Assessment
Methodologies Competences Description Weight        
Presentations / expositions
A5
G2
CT2
CT5
 Presentation of the results of the practical exercise. 30%
Assignments
A1
G1
CT3
CT4
CT7
 Realització individual d’exercicis pràctics usant les TIC 50%
Objective multiple-choice tests
A9
 Short test-style questions on the theoretical content of the course. 20%
Others  
 
Other comments and second exam session

The second call will have the same evaluation than the first one.

Sources of information

Basic A. Mas , Agentes software y sistemas multi-agente , Pearson-Prentice Hall , 2005
M.Wooldridge , An introduction to multiagent systems (2nd ed) , Wiley , 2009

Complementary , Info. plana web JADE , ,
, Journal of Autonomous Agents and Multi-Agent Systems , ,
Isern, Sánchez , Guia de programació de sistemes multiagent en JADE 3.3 , DEIM-RT-05-001 , 2005
G.Weiss , Multiagent Systems. A Modern Approach to Distributed Artificial Intelligence , MIT Press , 1999
M.Fasli , Agent technology for e-commerce , Wiley, 2007

Recommendations
(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.