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, José António Barata de Oliveira
Hours
Weekly - 4
Total - 62
Teaching language
Português
Prerequisites
Available soon
Bibliography
Course handouts:
-Ana Inês Oliveira, João Rosas, 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/
- set of bibliography suggested during classes
Teaching method
Available soon
Evaluation method
The UC includes a theoretical component and a practical component. Theoretical classes are directed so that students, through their active participation, understand each of the topics listed in the learning objectives. In practical classes students focus on experimenting with the concepts exposed in theoretical classes in order to know how to do it. A set of practical works is proposed, where each one will be carried out:
- Presentation of the work
- Tutorial on technologies and tools to be used
- Discussion of the working method to be adopted
- Realization of the work by the students, report preparation and presentation
Evaluation Components: 2 Tests and 2 Practical Works
Evaluation Rules:
1. Theoretical Grade: NT = NTest1 * w1 + NTtest2 * w2 -> (w1=w2=50%)
2. Practical Grade: NP = TPratico1 * p1 + TPratico * p2 -> (p1=60%, P2=40%)
3. Final Grade: NF = NT * 0.5 + NP * 0.5
4. NT >= 9.5; NP >= 9.5
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.