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. Web:moodle URV Mail:
Person in charge
Aida Valls (
Technical Competences of each Specialization
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.
CEP3 - Capacity for applying Artificial Intelligence techniques in technological and industrial environments to improve quality and productivity.
Generic Technical Competences
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.
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.
Recognize the main components of a decision making problem and decide the most appropriate modelization method.
Build a preference model according to the heterogeneous data types.
Make an appropriate selection and use of aggregation operators.
Study and apply methods based on the Multi-Attribute Utility Theory.
Study and apply methods based on Outranking models for MCDA.
Identify the relations between MCDA (Multi-criteria Decision Aiding) and AI (Artificial Intelligence)
1.1 The decision making problem. Formalization.
1.2 MCDA applications
2 Preference representation models
2.1 Data types
2.2 Family of criteria
3 Multi-Attribute Utility Theory
3.2 Steps: aggregation and exploitation.
3.3 Aggregation operators. Properties.
4 Models based on outranking relations
4.2 Outranking relations
5 MCDA and AI
Use of MCDA in combination with other intelligent techniques, like intelligent recommender systems.
The lecturer explains the theoretical conceps of the subject with examples.
Some complementary materials will be given to the students.