Personalized Multicriteria Decision Support Systems

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Credits
4.5
Types
Elective
Requirements
This subject has not requirements

Department
URV
Web
moodle URV
Mail
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).
Free software will be used to practise.

Teachers

Person in charge

  • Aida Valls ( )

Weekly hours

Theory
1.8
Problems
0
Laboratory
0.9
Guided learning
0
Autonomous learning
4.5

Competences

Generic Technical Competences

Generic

  • 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

  • CEA12 - Capability to understand the advanced techniques of Knowledge Engineering, Machine Learning and Decision Support Systems, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.

Professional

  • CEP3 - Capacity for applying Artificial Intelligence techniques in technological and industrial environments to improve quality and productivity.

Transversal Competences

Solvent use of the information resources

  • CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.

Analisis y sintesis

  • CT7 - Capability to analyze and solve complex technical problems.

Objectives

  1. Recognize the main components of a decision making problem and decide the most appropriate modelization method.
    Related competences: CEA12, CG3, CEP3,
  2. Build a preference model according to the heterogeneous data types.
    Related competences: CT7, CEA12,
  3. Make an appropriate selection and use of aggregation operators.
    Related competences: CEA12, CEP3,
  4. Study and apply methods based on the Multi-Attribute Utility Theory.
    Related competences: CT4, CT7, CEA12, CEP3,
  5. Study and apply methods based on Outranking models for MCDA.
    Related competences: CT4, CT7, CEA12, CEP3,
  6. Identify the relations between MCDA (Multi-criteria Decision Aiding) and AI (Artificial Intelligence)
    Related competences: CEA12, CEP3,

Contents

  1. 1 Introduction
    1.1 The decision making problem. Formalization.
    1.2 MCDA applications
  2. 2 Preference representation models for user profiles
    2.1 Data types
    2.2 Family of criteria
    2.3 User profile construction and update
  3. 3 Multi-Attribute Utility Theory
    3.1 Introduction
    3.2 Steps: aggregation and exploitation.
    3.3 Aggregation operators. Properties.
  4. 4 Models based on outranking relations
    4.1 Introduction
    4.2 Outranking relations
    4.3 ELECTRE
  5. 5 MCDA and AI
    Use of MCDA in combination with other intelligent techniques, like intelligent recommender systems.

Activities

Lectures

The lecturer explains the theoretical conceps of the subject with examples. Some complementary materials will be given to the students.
Theory
27
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
26
Objectives: 1 2 6
Contents:

Practical exercises at the computer lab

The student will use a free software to solve some exercises. Some of them will be reported in a short document delivered to the teacher.
Theory
0
Problems
0
Laboratory
12
Guided learning
0
Autonomous learning
12
Objectives: 5 2 3 4
Contents:

Teaching methodology

Oral exposition of the teacher
Oral presentations of the students
Practical exercices with software tools
Solving exercices in class

Evaluation methodology

Solving practical exercices with software tools 30%
Research report with an oral presentation 30%
Exam with short questions 40%

* The student must reach a minimum qualification of 5 (out of 10) in the exam in order to pass the course.
* There will be a second evaluation call for the exam. Again a minimum grade of 5 is required.

Bibliografy

Basic:

  • Multiple criteria decision analysis : state of the art surveys - Figueira, José; Greco, Salvatore; Ehrgott, Matthias, Springer , c2005. ISBN: 978-0-387-23067-2
    http://cataleg.upc.edu/record=b1269711~S1*cat
  • Modeling decisions : information fusion and aggregation operators - Torra i Reventós, Vicenç; Narukawa, Yasuo, Springer , cop, 2007. ISBN: 978-3-540-68789-4
    http://cataleg.upc.edu/record=b1308961~S1*cat
  • Multi-criteria Decision Analysis: Methods and Software - Alessio Ishizaka, Philippe Nemery, Wiley , 2013. ISBN: 978-1-119-97407-9
  • Multicriteria Decision Aid and Artificial Intelligence - Doumpos, Michel, Grigoroudis, Evangelos, Wiley , 2013. ISBN: 978-1-119-97639-4

Web links

Previous capacities

None