The issues of the course are to provide students with the basic and necessary knowledge, in order that after finishing the course, they could identify when a given domain is really a complex one, and how many and of which nature are the decisions involved in the management of the given domain. Also, a main goal is to know how to analyse, to design, to implement and to validate an Intelligent Decision Support Systems (IDSS), for this kind of domains. Particularly, the integration of Artificial Intelligence models and Statistical models, and the knowledge discovery from data step, will be emphasised.
Person in charge
Miquel Sanchez Marre (
Karina Gibert Oliveras (
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.
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.
CEP8 - Capability to respect the surrounding environment and design and develop sustainable intelligent systems.
CT3 - Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available 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.
CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.
CB7 - Ability to integrate knowledges and handle the complexity of making judgments based on information which, being incomplete or limited, includes considerations on social and ethical responsibilities linked to the application of their knowledge and judgments.
To provide students with the basic and necessary knowledge, in order that they could identify when a given domain is really a complex one
To identify how many and of which nature are the decisions involved in complex domains management
To know how to analyse, to design, to implement and to validate an Intelligent Decision Support Systems (IDSS), emphasising the integration of Artificial Intelligence models and Statistical/Numerical models, and the knowledge discovery from data.
Complexity of real-world systems or domains
The need of decision support tools
Modelling of Decision Process
Evolution of Decision Support Systems
Historical perspective of Management Information Systems
Decision Support Systems (DSS)
Advanced Decision Support Systems (ADSS)
Intelligent Decision Support Systems (IDSS)
Intelligent Decision Support Systems (IDSS)
IDSS Analysis and Design
Requirements, advantages and drawbacks of IDSS
Implementation of an IDSS in a computer
Knowledge Discovery in a IDSS: from Data to Models
- Descriptive models
- Associative models
- Discriminant Models
- Predictive models
- Probabilistic models
- Fuzzy models
Post-Processing and Model Validation
Statistical Methods for Hypotheses Verification
Tools and Applications
Software Tools for IDSS Development
Application of IDSS to real-world problems
Future Trends in IDSS and Conclusions
INTRODUCTION TO THE COURSE: General view, Contents, Web page, Racó, Evaluation, Practical works, etc.
INTRODUCTION TO THE IDSS: Complexity of Real-world Systems, Decision Theory.
The contents of the course will be exposed with the support of several case studies along the course. In the laboratory classes, the homework of the students (practical works) will be supervised by the teacher.
Evaluation of the knowledge and skills obtained by the students will be assessed through the 3 Practical Works. The final grade will be the weighted mean of the grade of each practical work. Each practical work will have the following weights:
PW1 -> 25%
PW2 -> 25%
PW3 -> 50%
Thus, the final grade will be computed as follows:
WFstud is a Working Factor evaluating the work of a particular student within his/her teamwork in PW3. It will be obtained by observing and assessing the load of work and degree of participation of each student throughout the PW3. In normal conditions, the WFstud = 1.
The PW1 will be evaluated by means of its quality and its justified explanation in the document. The PW2 will be evaluated according to its accuracy and completeness.The PW3 will be evaluated through:
- The quality of the methodology and work done (0.4)
- The documentation delivered (0.2),
- The quality of the oral exposition (both presentation and content assessed, as well as the ability to answer questions) (0.2)
- The planification, coordination and management of the team (0.05)
- The individual valoration of each student, including her/his integration level within the teamgroup (0.15)