Faculdade de Ciências e Tecnologia
Departamento de Engenharia Electrotécnica
Teacher in charge
Luís Manuel Camarinha de Matos
To provide students with:
1) Knowledge on: a) Base concepts of intelligent supervision. b) Various techniques of planning, monitoring, diagnosis, error recovery and machine learning. C) Analysis of requirements for supervision systems.
2) Know-how on: a) Capacity to integrate multi-disciplinary knowledge. b) Capability to model supervision problems and select tools. c) Capability to solve problems in new contexts.
3) Non-technical competences: a) Experimentation skills. b) Time management and deadline fulfillment skills.
1. INTRODUCTION AND MOTIVATION.
2. INTRODUCTION TO PLANNING. Principles and methods. Relationship between planning and supervision.
3. SUPERVISION ARCHITECTURES: Despatch, Monitoring, Diagnosis, Error recovery, Prognosis. Advanced concepts.
4. REAL TIME EXPERT SYSTEMS: Base concepts. Architectures. Examples of systems. Development tools.
5. QUALITATIVE REASONING. Concepts and methods. Examples.
6. MACHINE LEARNING IN SUPERVISION: Inductive methods. Neural networks. Genetic algorithms. EBL. Application examples.
- Selected papers.
Theoretical part: Concept explanation lectures followed by examples and discussion.
Laboratorial component: For each experiment: Introduction to the planned work, tutorial on the technologies / tools to be used, discussion of the method of work, development of the experiments by the students supervised by a teaching assistant; elaboration of a report.
Written exam (65%) + Evaluation of Lab experiments (35%).
Each component must reach a minimum grade of 9.5.