Intelligent Decision Support Systems (IDSS)

Master in Artificial Intelligence (UPC-URV-UB)

Fall semester, Course 2019/2020

(4.5 ECTS, 40.5 hours)

Responsible Professor:

Miquel Sànchez i Marrè
Knowledge Engineering & Machine Learning Group
Dept. of Computer Science
Universitat Politècnica de Catalunya · BarcelonaTech
Campus Nord - Building OMEGA, 2nd floor, office 203


Professor  Office
E-mail  Phone
Miquel Sànchez i Marrè OMEGA building,
  2nd floor, office 203
Campus Nord 93 413 78 41
To convene by e-mail
Karina Gibert
C5 building,
  2nd floor, office 219
Campus Nord
93 413 73 23
To convene by e-mail


Day Hour Room
14-17 A5 BUILDING, 2nd floor, A5201
M. Sànchez/K. Gibert

Course schedule:

Lecturing periodFrom September 9th 2019 to December 16th 2019
Holidays: September 23th, 2019 (bank holiday), December 23rd 2019 - January 6th 2020 (Christmas)

Schedule Changes: None


Complexity of real-world systems or domains
The need of decision support tools

Decision Theory
Modelling of Decision Process

Evolution of Decision Support Systems 
Historical perspective of Management Information Systems
Decision Support Systems (DSS) 
Advanced Decision Support Systems
Intelligent Decision Support Systems (IDSS)

Intelligent Decision Support Systems (IDSS)
IDSS Architecture
IDSS Analysis and Design
Requirements, advantages and drawbacks of IDSS 
IDSS Validation
Implementation of an IDSS in a computer: Environmental Systems Management (an example)
The Use of Intelligent Models in Decision Support
Knowledge Discovery and Data Mining
Artificial Intelligence and Statistics
Data Structure
Pre-Processing Techniques
Knowledge Models
Descriptive Models 
(AI) Conceptual Clustering Techniques
(Stats) Statistical Clustering Techniques
(AI&Stats) AI&Stats Hybrid Techniques: Classification Based on Rules (ClBR)
uAssociative Models 
(AI): Association Rules, Model-Based Reasoning, Qualitative Reasoning
(Stats): Principal Component Analysis (PCA), Simple Correspondence Analysis (SCA), Multiple Correspondence Analysis (MCA)
(AI&Stats) Bayesian (Belief) Networks
Discriminant Models 
Rule-Based Reasoning 
(AI) Decision Trees 
(AI) Classification Rules 
(Stats) Discriminant Analysis
(AI&Stats) Box-plot Based Induction Rules, Random Forests
Case-Based Reasoning 
(AI) Instance-Based Learning (IBL)
Bayesian Learning
        Naive-Bayes Classifier
Statistical Classifiers
        Support Vector Machines (SVMs)
uPredictive Models
(AI) Connexionist Models (ANN)
(AI) Evolutionary Computation (GAs, GP)
(AI) Collaborative Resolution (“Swarm intelligence“)
(Stats) Multiple sand Simple Linear Regression Models, Variance Analysis, Time Series Models
(AI&Stats) Regression Trees, Model Trees, Random Forests
        Uncertainty Models
                (Stats) Pure Probablistic Model
                (AI&Stats) Bayesian Networks
                (AI) Certainity Factor Model
                (AI) Possibilistic Model (Fuzzy Logic)

Post-processing and Model Validation
        Post-processing Techniques
    Graphical Validation Tools
    Validation methods for Predictive and Discriminant Models
    Validation methods for Descriptive and Associative Models
Statistical Methods for Hypotheses Verification

Software Tools for IDSS Development 

Future Trends in IDSS and Conclusions

Learning Resources
Literature References
Internet Resources

Course material

The “Racó de la FIB” is the Virtual Campus for you. There, you will find any announcement, change in the schedule, the practical works, the results of the evaluation of the works and final marks, etc. There you will also find a pointer to this WEB page of the course where you could find some other material (slides, internet references, etc.) in electronic format

2019/2020 Sylabus:
                    Part 1 - Introduction/Decision Theory
                Part 2 - Evolution of DSS/IDSS
                    Part 3 - KDD in IDSS, Preprocessing I, Preprocessing II, Data Mining methods Conceptual Map (DMMCM)
                Part 4 - PostProcessing and Model Validation
art 5 - Intelligent Models in Decision Support: Descriptive Models, Assessing intervention plans in mental health for Low and Middle income countries

                Part 6 - Intelligent Models in Decision Support: Discriminant Models
               Part 7 - Intelligent Models in Decision Support: Predictive Models (I), Predictive Models (II)
                Part 8 - Intelligent Models in Decision Support: Associative Models
               Part 9 - Uncertainty Models
                Part 10 - Tools
                Part 11 - Future Trends in IDSS/Conclusions

                Case Study 1 - Implementation of an IDSS in a computer (Customer Fidelization Case)
                Case Study 2 - The use of Descriptive Models for Decision Support (Medical/Health Case)
                Case Study 3 - The use of Discriminant Models for Decision Support (I) (Optimal Wastewater Treatment System Assignment Case)
                Case Study 4 - The use of Discriminant (II)/Predictive Models for Decision Support (I) (The Textile Industry Case)
                Case Study 5 - The use of Predictive Models for Decision Support (II) (Regression, ANNs Case)
                Case Study 6 - The use of Associative Models for Decision Support (I) (Market Basket Analysis Case)
                Case Study 7 - The use of Associative Models for Decision Support (II) (BayesNet Case, including logistic predictive models)
                    Case Study 8 - The use of Uncertainty Models (Wastewater Treatment Plant Management Case)

Relevant References:

Papers directly linked with the contents of the course:

Linked with Part 3: Preprocessing:
Survey on preprocessing
Missing imputation: The MiMMI method

Linked with Part 5 to 8: Intelligent Models in Decision Support
Data Mining Method Conceptual Map

Linked with Case Study 7:
Case 7 Bayesian Networks for Predictions

Linked with the working teams:
Assertive behaviour
Hitchhikers and Couch Potatoes

Practical exercises

Three practical works must be done by all students in the course (PW1, PW2 and PW3). The practical works are:

PW1 (Individual work) - Modelling a decision making process in a concrete domain.
PW1 will be given on: September 16th, 2019
PW1 is due on: September 30st, 2019

PW2 (Individual work) - Reviewing the state of the art of Intelligent Decision Support Systems.
PW2 will be given on: September 16th, 2019
PW2 is due on: October 14th, 2019

Find in the Papers Archive the list of papers analyzed in previous editions of the course till course 2017-2018
The collection of papers is here:
        IDSS-Papers Archive 1213
        IDSS-Papers Archive 1314
        IDSS-Papers Archive 1415
        IDSS-Papers Archive 1617
        IDSS-Papers Archive 1718
        IDSS-Papers Archive 1819
        IDSS-Papers Archive 1920

PW3 (Group work) - A practical work to build an Intelligent Decision Support System
PW3 will be given on: October 14th, 2019
PW3 is due on: December 15th, 2019
        PW3 public presentation & discussion on: December 16th, 2019

Resources for a good performance as a working team

Working Team resources
Turning Students groups into effective teams
Assertivity in working teams (adapted from [Oaklay2004])
Assertivity in working, Montañé
How to deal with dysfunctional members
Comunicación efectiva en el trabajo


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 -> 2
PW3 -> 50%

Thus, the final grade will be computed as follows:

      FinalGrade = 0.25*PW1Grade + 0.25*PW2Grade + 0.
5*PW3Grade * WFstud                0 ≤ WFstud ≤ 1.2


where 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

Evaluation of the course performance will be based on three marks coming from the three exercises.

The first exercise will be evaluated by means of its quality and its justified explanation in the document, and the quality of the oral exposition. The second exercise will be evaluated according to its accuracy and completeness, and the quality of the oral exposition.

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 planning, coordination and management of the team (0.05)

- The individual evaluation of each student, including her/his integration level within the team group (0.15)

Honour Code (adapted from Stanford University Honor Code)

A.    The Honor Code is an undertaking of the students, individually and collectively:
  1. that they will not give or receive aid in examinations; that they will not give or receive unpermitted aid in class work, in the preparation of reports, or in any other work that is to be used by the instructor as the basis of grading;
  2. that they will do their share and take an active part in seeing to it that others as well as themselves uphold the spirit and letter of the Honor Code.
B.    The faculty on its part manifests its confidence in the honor of its students by refraining from proctoring examinations and from taking unusual and unreasonable precautions to prevent the forms of dishonesty mentioned above. The faculty will also avoid, as far as practicable, academic procedures that create temptations to violate the Honor Code.

C.    While the faculty alone has the right and obligation to set academic requirements, the students and faculty will work together to establish optimal conditions for honorable academic work.

Examples of conduct which have been regarded as being in violation of the Honor Code include:

•    Copying from another’s examination paper or allowing another to copy from one’s own paper
•    Unpermitted collaboration
•    Plagiarism
•    Revising and resubmitting a quiz or exam for regrading, without the instructor’s knowledge and consent
•    Giving or receiving unpermitted aid on a take-home examination
•    Representing as one’s own work the work of another
•    Giving or receiving aid on an academic assignment under circumstances in which a reasonable person should have known that such aid was not permitted

In recent years, most student disciplinary cases have involved Honor Code violations; of these, the most frequent arise when a student submits another’s work as his or her own, or gives or receives unpermitted aid. The standard penalty for a first offense or multiple violations will be proposed by the teachers in charge of the course, and approved by the Academic Commission of the Master MAI.

Resources for student learning


        Updated list of references

Internet Resources

Catalan Statistics Institute (Institut d'Estadística de Catalunya) 
European Statistics Office
UCI Machine Learning Repository
Knowledge Discovery Network of Excellence
Data Mining Resources
Data Mining Sites
R Software 
WEKA Software
GESCONDA software