IDENTIFYING DATA 2016_17
Subject (*) MULTI-CRITERIA DECISION MAKING SYSTEMS Code 17685108
Study programme
Computer Security Engineering and Artificial Intelligence (2016)
 Cycle 2nd
Descriptors Credits Type Year Period
4.5 Compulsory First 2Q
Language
Anglès
Department Computer Engineering and Mathematics
Coordinator
VALLS MATEU, AÏDA
 E-mail aida.valls@urv.cat
Lecturers
VALLS MATEU, AÏDA
Web
General description and relevant information The student will be introduced to the research area of Multicriteria Decision Aid (MCDA). The course covers three main issues: (1) Preference structures for representing the interests of the decision maker. Special attention will be paid to the use of non-numerical information, such as linguistic variables, fuzzy sets or ontologies. (2) Exploitation techniques of the user information to solve the decision problem. The two main approaches to MCDA will be studied: Multiattribute Utility Theory and Outranking Relations. At the end of the course, the student will have to know the theory, properties, advantages and drawbacks of those methods. (3) Use of MCDA techniques in combination with other fields (f.i. Geographical Information Systems, Recommender Systems).

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.
 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 Dissenya tecnologies de garantia de la privacitat per a escenaris d'aplicacions informàtiques i telemàtiques.
 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
1. Introduction to Multiple Criteria Decision Analysis (MCDA)
2. Preference modelling 2.1 Variables and criteria
2.2 User profile with numerical data
2.3. User profile with categorical data
3. The Multi-Attribute Utility Theory 3.1 Basic concepts
3.2 Aggregation operators for numerical and linguistic criteria
4. Outranking methods for pairwise preference relation 4.1 Basic concepts
4.2 ELECTRE method
5. Advanced techniques of MCDA using AI

Planning
Methodologies  ::  Tests
  Competences (*) Class hours
 Hours outside the classroom
 (**) Total hours
Introductory activities
1 1.5 2.5
Lecture
A1
A5
 26 41.5 67.5
Presentations / expositions
G1
CT5
 1 1.5 2.5
Practicals using information and communication technologies (ICTs) in computer rooms
A1
A9
G2
 10 15 25
ICT practicals
A1
G2
CT2
CT3
CT4
CT7
 4 6 10
Personal tuition
1 0 1
 
Objective short-answer tests
A5
G2
 2 2 4
 
(*) 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 teachers, the goals of the course and the evaluation procedure.
Lecture Each week some lectures will be given, with materials available in advance in Moodle.
Presentations / expositions The student will collect research papers about a certain topic (proposed by the teacher) and will make an overview and comparison. The resulting report will be evaluated. The work will also be explained in an oral presentation to the rest of students.
Practicals using information and communication technologies (ICTs) in computer rooms Students will solve practical case studies using public software tools, or programming some algorithms.
ICT practicals The student must solvesome exercises in class (in the lab). A report will be delivered by the student, in some exercises.
Personal tuition The student will attend questions at her office (previously arrangement by email). Questions can also be solved by email directly.

Personalized attention
Description
The student will attend questions at her office (previously arrangement by email). Questions can also be solved by email directly.

Assessment
Methodologies Competences Description Weight        
Presentations / expositions
G1
CT5
 Oral exposition of a research work. 30%
Practicals using information and communication technologies (ICTs) in computer rooms
A1
A9
G2
 Solving exercises in class (in the lab). Participation in class will also be considered.
 10%
ICT practicals
A1
G2
CT2
CT3
CT4
CT7
 Student will solve practical case studies using public software tools, or programming some algorithms.
 20%
Objective short-answer tests
A5
G2
 Final exam. A minimum qualification of 5 is required to pass the course. 40%
Others  
 
Other comments and second exam session

Sources of information

Basic Figueira, J., Greco, S., Ehrgott, M (eds), Multiple Criteria Decision Analysis, Springer, 2005
Ishizaka, A., Nemery, P., Multi-criteria decision analysis: methods and software, Wiley, 2013
Torra, V., Narukawa, Y., Modelling Decisions: Information fusion and Aggregation operators, Springer , 2005

Complementary http://www.cs.put.poznan.pl/ewgmcda/, Euro working group on MCDA, ,
http://www.mcdmsociety.org/, Int Society on MCDM, ,
Matthias Ehrgott, José Rui Figueira and Salvatore Greco, Trends in Multiple Criteria Decision Analysis, Springer, 2010
Doumpos, M., Grigoroudis, E. , Multicriteria Decision Aid and Artificial Intelligence , Wiley , 2013

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.