IDENTIFYING DATA 2019_20
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
najlaamaaroofwahib.al-ziyadi@urv.cat
Lecturers
VALLS MATEU, AÏDA
AL-ZIYADI , NAJLAA MAAROOF WAHIB
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 Forming opinions on the basis of the efficient management and use of information
 CT3 Solve complex problems critically, creatively and innovatively in multidisciplinary contexts.
 CT4 Work in multidisciplinary teams and in complex contexts.
 CT5 Communicate complex ideas effectively to all sorts of audiences
 CT7 Apply ethical principles and social responsibility as a citizen and a professional.
Type C Code Competences Nuclear

Learning outcomes
Type A Code Learning outcomes
 A1 Analyse the problems and their causes from a global focus in the medium and long term.
 A5 Identify the components of a decision-making problem and know how to decide the most suitable decision-making model.
 A9 Design technology to guarantee privacy for scenarios of IT and telematics applications.
 G1 Integrate theoretical knowledge into the realities to which it may apply.
 G2 Apply the techniques learned in a specific context.
Type B Code Learning outcomes
 CT2 Manage information and knowledge by making efficient use of the information technologies.
 CT3 Recognise the situation as a problem in a multidisciplinary, research or professional environment, and take an active part in finding a solution.
 CT4 Participate in the group in a good working environment and help to solve problems.
 CT5 Produce a persuasive, consistent and precise discourse that can explain complex ideas and effectively interact with the audience.
 CT7 Apply ethical and socially responsible principles as a citizen and 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 / oral communications
G1
CT5
 1 1.5 2.5
IT-based practicals in computer rooms
A1
A9
G2
 10 15 25
IT-based practicals
A1
G2
CT2
CT3
CT4
CT7
 4 6 10
Personal attention
1 0 1
 
Short-answer objective 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 / oral communications 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.
IT-based practicals in computer rooms Students will solve practical case studies using public software tools, or programming some algorithms.
IT-based practicals The student must solvesome exercises in class (in the lab). A report will be delivered by the student, in some exercises.
Personal attention 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 / oral communications
G1
CT5
 Oral exposition of a research work. 30%
IT-based practicals in computer rooms
A1
A9
G2
 Solving exercises in class (in the lab). Participation in class will also be considered.
 10%
IT-based practicals
A1
G2
CT2
CT3
CT4
CT7
 Student will solve practical case studies using public software tools, or programming some algorithms.
 20%
Short-answer objective tests
A5
G2
 Final exam with questions and problems 40%
Others  
 
Other comments and second exam session

If any activity is not accepted, it should be repeated.

During the exams, no communication or data transmission device can be used.


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.