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 - 36
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 2h. 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:
- 2 practical project assignments (TP1 and TP2), with oral discussion and with a written technical report (35%)
- 1 mid-term test during the course (T) (30%)
The formula for the calculation of the final grade:
- Final Grade = 30%T + 30%TP1 + 40%TP2
To succeed students must obtain:
(TP1,TP2 >=9.5 V) AND T >= 9.5 V)
Students must do 2 project assignments (TP) to be eligible to perform a test and or exam.
1 Exam (for students without approval in the written test).
Subject matter
- Introduction to Artificial Intelligence
- Knowledge Management and Knowledge Engineering
- Decision support systems and Expert systems
- Architecture
- Knowledge Engineering Methods
- Inference processes
- Approximate Reasoning based on Fuzzy Logic
- Fuzzy Decision support systems
- Architecture
- Approximate reasoning modelling using fuzzy
- Fuzzy inference – Mamdani method
- Introduction to knowledge data discovery methods
- Intelligent decision support systems based on data
- Machine learning/data mining methods for knowledge extraction