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Intelligent Supervision

Objectives

This unit aims 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 multidisciplinary 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.

General characterization

Code

7228

Credits

6.0

Responsible teacher

Ana Inês da Silva Oliveira, Luís Manuel Camarinha de Matos

Hours

Weekly - 4

Total - 62

Teaching language

Português

Prerequisites

Available soon

Bibliography

Course handouts:
- L. M. Camarinha-Matos, Notas de Supervisão Inteligente.
- Tutorial RT-EXPERT, BellHawk Systems Corporation, 2005
http://www.bellhawk.com/Product_Info/user_manuals/RT-Expert_Tutorial27Feb05.pdf
- Aris Corp. RT-Expert Programming Manual, 1996.
- University of Amsterdam. GARP3 - Qualitative Modeling & Reasoning. http://hcs.science.uva.nl/QRM/software/
Conjunto de publicações selecionado. Exemplos: / Selected articles. Examples:
- K. Moslehi, R. Kumar. Vision for a self-healing power grid. ABB Review 4, 2006.
- NETICA Belief Networks Software, http://www.norsys.com/

Teaching method

Available soon

Evaluation method

Available soon

Subject matter

1. INTRODUCTION: Concepts of plan and goal. Concept of supervision.
2. REAL TIME EXPERT SYSTEMS: Main characteristics of a real time ES.
3. PLANNING AND SUPERVISION: Base concepts of Planning. Execution. Interaction planner / executor.
4. SUPERVISION ARCHITECTURES: General architecture of a supervisor. Main functionalities: Dispatch and monitoring, Diagnosis, error recovery. Additional functionalities: Prognosis, preventive maintenance support. Representation of errors and exceptions: Taxonomies, causal diagrams. Multilevel architectures. Knowledge based systems: Condition - Action rules; asynchronism, blackboard and multiagent architectures. Supervision Application Examples.
5. MACHINE LEARNING IN SUPERVISION: Need for machine learning in supervision. Overview of machine learning techniques for supervision systems.