Intelligent Decision Support Systems

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

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, CEP3, CEP8, CG3,

    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
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    PW2 public presentation & discussion


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

    PW3 public presentation & discussion


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

    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.