Intelligent Decision Support Systems

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Credits
4.5
Types
Elective
Requirements
This subject has not requirements, but it has got previous capacities
Department
EIO;CS
Web
https://www-eio.upc.edu/teaching/DocenciaMultivariant/IDSS/
Mail
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.

Teachers

Person in charge

  • Karina Gibert Oliveras ( )

Others

  • Xavier Angerri Torredeflot ( )

Weekly hours

Theory
2
Problems
0
Laboratory
1
Guided learning
0.115
Autonomous learning
5.53

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.
  • CEP8 - Capability to respect the surrounding environment and design and develop sustainable intelligent systems.

Transversal Competences

Teamwork

  • 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.

Information literacy

  • 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.

Basic

  • 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.

Objectives

  1. To provide students with the basic and necessary knowledge, in order that they could identify when a given domain is really a complex one
    Related competences: CB6, CB7, CT7, CEA12,
  2. To identify how many and of which nature are the decisions involved in complex domains management
    Related competences: CT7, CEA12, CB7, CB6,
  3. 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.
    Related competences: CT3, CT4, CG3, CEP3, CEP8,

Contents

  1. Introduction
    Complexity of real-world systems or domains
    The need of decision support tools
  2. Decisions
    Decision Theory
    Modelling of Decision Process
  3. 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)
  4. 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
  5. Knowledge Discovery in a IDSS: from Data to Models
    Introducction
    Data Structure
    Data Filtering
    Knowledge Models
    - Descriptive models
    - Associative models
    - Discriminant Models
    - Predictive models
    Uncertainty Models
    - Probabilistic models
    - Fuzzy models
  6. Post-Processing and Model Validation
    Post-processing techniques
    Validation
    Statistical Methods for Hypotheses Verification
  7. Tools and Applications
    Software Tools for IDSS Development
    Application of IDSS to real-world problems
  8. Future Trends in IDSS and Conclusions

Activities

Activity Evaluation act


INTRODUCTION TO THE COURSE: General view, Contents, Web page, Racó, Evaluation, Practical works, etc.



Theory
1h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

INTRODUCTION TO THE IDSS: Complexity of Real-world Systems, Decision Theory.


Objectives: 1 2
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

PRESENTATION OF INDIVIDUAL PRACTICAL WORK 1 (PW1) and OF INDIVIDUAL PRACTICAL WORK 2 (PW2)


Objectives: 2 3
Theory
0h
Problems
0h
Laboratory
1h
Guided learning
0h
Autonomous learning
0h

PRESENTATION OF GROUP PRACTICAL WORK 3 (PW3). INTRODUCTION TO GESCONDA TOOL.


Objectives: 2 3
Theory
0h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
0h

EVOLUTION OF DECISION SUPPORT SYSTEMS: Decision Support Systems (DSS) and Advanced Decision Support Systems (ADSS)


Objectives: 3
Theory
1h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

INTELLIGENT DECISION SUPPORT SYSTEMS (IDSS): architecture, analysis and design, implementation


Objectives: 3
Theory
1h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Presentation of several Case Studies showing the design and develomentof IDSS


Objectives: 1 2 3
Theory
15h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

THE USE OF INTELLIGENT MODELS: Knowledge Discovery process.


Objectives: 3
Theory
4h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

PW3 supervision


Objectives: 1 2 3
Theory
0h
Problems
0h
Laboratory
8h
Guided learning
0h
Autonomous learning
0h

FUTURE TRENDS IN IDSS


Objectives: 3
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

PW1 public presentation & discussion


Objectives: 1 2
Week: 4
Theory
0h
Problems
0h
Laboratory
1h
Guided learning
0h
Autonomous learning
4h

PW2 public presentation & discussion


Objectives: 2 3
Week: 6
Theory
0h
Problems
0h
Laboratory
1h
Guided learning
0h
Autonomous learning
8h

PW3 public presentation & discussion


Objectives: 3
Week: 16
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
1.5h
Autonomous learning
60h

Teaching methodology

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 methodology

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:

FinalGr = 0.25*PW1Gr + 0.25*PW2Gr + 0.5*PW3Gr * WFst, where 0 ≤ WFst ≤ 1.2

where WFst 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 WFst = 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 following formula:

PW3Gr = 0.4*MetGr + 0.2*DocGr + 0.2*OrEGr + 0.05*TManGr + 0.15*IGr

Where:
- MetGr: Grade for the quality of the methodology and work done, DocGr: Grade for the documentation delivered, OrEGr: Grade for the quality of the oral exposition (both presentation and content assessed, as well as the ability to answer questions), TManGr: Grade for the planning, coordination and management of the team, IGr: The individual evaluation of each student including her/his integration level within the team group.
This individual student grade (IGr) will be a mean between the teacher assessment of the student (TeachA) and the self-assessment of the student participation by the other members of the team (SelfA). Thus,

IGr = 0.5*TeachA+ 0.5*SelfA

Bibliography

Basic:

Previous capacities

Fundamentals on Machine Learning and Artificial Intelligence.