IDENTIFYING DATA

2020_21

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

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

<p> The information published in this guide corresponds to face-to-face classes and can serve as a guide. Due to the health emergency caused by COVID-19 there may be changes in teaching, assessment and calendars for the 2020-21 academic year. These changes will be reported in the Moodle space of each subject.</p><p>GENERAL DESCRIPTION OF THE SUBJECT: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). </p>

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	Master the tools for managing their own identity and activities in a digital environment. Search for and find information autonomously using criteria of importance, reliability and relevance, which is useful for creating knowledge Organise information with appropriate tools (online and face-to-face) so that it can be updated, retrieved and processed for re-use in future projects. Produce information with tools and formats appropriate to the communicative situation and with complete honesty. Use IT to share and exchange the results of academic and scientific projects in interdisciplinary contexts that seek knowledge transfer.
	CT3	Recognise the situation as a problem in a multidisciplinary, research or professional environment, and take an active part in finding a solution. Follow a systematic method with an overall approach to divide a complex problem into parts and identify the causes by applying scientific and professional knowledge. Design a new solution by using all the resources necessary and available to cope with the problem. Draw up a realistic model that specifies all the aspects of the solution proposed. Assess the model proposed by contrasting it with the real context of application, find shortcomings and suggest improvements.
	CT4	Understand the team’s objective and identify their role in complex contexts. Communicate and work with other teams to achieve joint objectives. Commit and encourage the necessary changes and improvements so that the team can achieve its objectives. Trust in their own abilities, respect differences and use them to the team’s advantage.
	CT5	Produce quality texts that have no grammatical or spelling errors, are properly structured and make appropriate and consistent use of formal and bibliographic conventions Draw up texts that are structured, clear, cohesive, rich and of the appropriate length, and which can transmit complex ideas. Draw up texts that are appropriate to the communicative situation, consistent and persuasive. Use the techniques of non-verbal communication and the expressive resources of the voice to make a good oral presentation. Construct a discourse that is structured, clear, cohesive, rich and of the appropriate length, and which can transmit complex ideas. Produce a persuasive, consistent and precise discourse that can explain complex ideas and effectively interact with the audience.
	CT7	Be aware of gender and other inequalities in their activity as a URV student. Analyse the major environmental problems from the perspective of their field of expertise in their student and/or professional activity. Be able to give arguments based on social values and make proposals for the improvement of the community. Be personally and professionally committed to applying the ethical and deontological concepts of their field of expertise.
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

1.5

2.5

Lecture

41.5

67.5

Presentations / oral communications

CT5

1.5

2.5

IT-based practicals in computer rooms

IT-based practicals

Personal attention

Short-answer objective tests

(*) 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

CT5

Oral exposition of a research work.

30%

IT-based practicals in computer rooms

Solving exercises in class (in the lab). Participation in class will also be considered.

10%

IT-based practicals

Student will solve practical case studies using public software tools, or programming some algorithms.

20%

Short-answer objective tests

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.