Intelligent Systems for Decision Support

Objectives

The aim of this course is to teach students the theoretical foundations of artificial intelligence, systems architecture and the main approaches used in intelligent systems. Particular emphasis is given to the basic principles of approximate reasoning based on fuzzy logic and its application to modeling, control and decision making, combining qualitative and quantitative data. The case studies reflect situations of decision making in uncertain and/or complex environments in the context of Industrial Engineering.

General characterization

Code

10612

Credits

3.0

Responsible teacher

Isabel Maria Nascimento Lopes Nunes, Pedro Emanuel Botelho Espadinha da Cruz

Hours

Weekly - 2

Total - 50

Teaching language

Português

Prerequisites

Not required.

Bibliography

  • Gupta, J. N. D., G. A. Forgionne, Mora, M.T. (2010). Intelligent Decision-making Support Systems: Foundations, Applications and Challenges (Decision Engineering), Springer.
  •  Ross, T. J. (2010). Fuzzy Logic with Engineering Applications, John Wiley.
  • Turban, E., R. Sharda, et al. (2010). Decision Support and Business Intelligence Systems, Prentice Hall.
  • Sivanandam, S. N,. Deepa S. N., Sumathi S., Introduction to Fuzzy Using Matlab, Springer, 2007
  • Zimmermann, H.-J. (2001). Fuzzy Set Theory and Its Applications, Kluwer Academic Publishers.
  • Zadeh, L. A. (1965). "Fuzzy sets." Information and Control 8(3): 338-353.

Teaching method

Theoretical-practical classes with a duration of 3h. Oral presentation of concepts, supported by multimedia teaching materials and accompanied by application to concrete cases where students take part individually or in groups.

Evaluation method

The evaluation process has the following components:

- 1 practical project-assignment (TP), with oral communication and with written technical report (35%)

- 1 mid-term online test during the course (T) (50%)

- In-class activities (TA) (15%)

Formula for the calculation of the final grade:

 - Final Grade = 50%T + 15%TA + 35%TP

To succeed students must obtain:

(T1,T2 >= 10 V) AND TP,TA  >= 10 V) 

 Students have to do, at least, 4 in-class activities (TA).

 1 Exam (for students without approval in written test).

 

Subject matter

  1. Introduction to Artificial Intelligence
  2. Knowledge Management and Knowledge Engineering
  3. Decision support systems and Expert systems
    • Architecture
    • Knowledge Engineering Methods
    • Inference processes
  4. Approximate Reasoning based on Fuzzy Logic
  5. Fuzzy Decision support systems
    • Architecture
    • Approximate reasoning modelling using fuzzy
    • Fuzzy inference – Mamdani method
  6. Introduction to knowledge data discovery methods
    • Intelligent decision support systems based on data
    • Machine learning/data mining methods for knowledge extraction

Programs

Programs where the course is taught: