Intelligent Control Methods Lecture 8: Knowledge Engineering Slovak University of Technology Faculty of Material Science and Technology in Trnava.

Slides:



Advertisements
Similar presentations
Lahore University of Management Sciences, Lahore, Pakistan Dr. M.M. Awais- Computer Science Department Lecture 2 Development Life Cycle of ES.
Advertisements

FT228/4 Knowledge Based Decision Support Systems Knowledge Engineering Ref: Artificial Intelligence A Guide to Intelligent Systems, Michael Negnevitsky.
ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information.
Object-Oriented Analysis and Design
So far.. We have covered a) Requirements gathering: observation & interview. b) Requirements specification. c) Requirements validation. d) Design/paper.
Artificial Intelligence
The Semantic Web Week 13 Module Website: Lecture: Knowledge Acquisition / Engineering Practical: Getting to know.
Knowledge Acquisitioning. Definition The transfer and transformation of potential problem solving expertise from some knowledge source to a program.
Copyright 2002 Prentice-Hall, Inc. Chapter 1 The Systems Development Environment 1.1 Modern Systems Analysis and Design Third Edition Jeffrey A. Hoffer.
School of Computing and Mathematics, University of Huddersfield Knowledge Engineering: Issues for the Planning Community Lee McCluskey Department of Computing.
Knowledge Acquisition CIS 479/579 Bruce R. Maxim UM-Dearborn.
Knowledge Acquisition. Knowledge Aquisition Definition – The process of acquiring, organising, & studying knowledge. Identified by many researchers and.
Supervised by, Mr. Ashraf Yaseen. Overview…. Brief Introduction about Knowledge Acquisition. How it can be achieved?. KA Stages. Model. Problems that.
Building Knowledge-Driven DSS and Mining Data
The RESEARCH PROCESS. IDEA-GENERATING PHASE  Research begins with an idea in which the researcher has interest.  It is in this phase wherein the researcher.
Software Engineering 2003 Jyrki Nummenmaa 1 REQUIREMENT SPECIFICATION Today: Requirements Specification Requirements tell us what the system should.
Copyright 2002 Prentice-Hall, Inc. Chapter 1 The Systems Development Environment 1.1 Modern Systems Analysis and Design.
SE-02 SOFTWARE ENGINEERING LECTURE 3 Today: Requirements Analysis Requirements tell us what the system should do - not how it should do it. Requirements.
Intelligent systems Lecture 10 Development of Expert Systems.
Marković Miljan 3139/2011
Copyright 2002 Prentice-Hall, Inc. Chapter 1 The Systems Development Environment 1.1 Modern Systems Analysis and Design Third Edition Jeffrey A. Hoffer.
1 Computer Group Engineering Department University of Science and Culture S. H. Davarpanah
Chapter 6 Supplement Knowledge Engineering and Acquisition Chapter 6 Supplement.
 Knowledge Acquisition  Machine Learning. The transfer and transformation of potential problem solving expertise from some knowledge source to a program.
Help Desk System How to Deploy them? Author: Stephen Grabowski.
School of Computing FACULTY OF ENGINEERING Developing a methodology for building small scale domain ontologies: HISO case study Ilaria Corda PhD student.
Software Engineering – University of Tampere, CS DepartmentJyrki Nummenmaa REQUIREMENT SPECIFICATION Today: Requirements Specification.
1 Expert System Design. 2 ES Development Methods commercial quality systems require a systematic development approach –ad hoc approaches may be suitable.
Using UML, Patterns, and Java Object-Oriented Software Engineering Chapter 4, Requirements Elicitation.
Requirements Engineering Requirements Elicitation Process Lecture-9.
Modeling and simulation of systems Model building Slovak University of Technology Faculty of Material Science and Technology in Trnava.
CS 4310: Software Engineering Lecture 4 System Modeling The Analysis Stage.
CSC 554: Knowledge-Based Systems Part-1 By Dr. Syed Noman Hasany Assistant Professor, CoC Qassim University.
1 CHAPTER 14 Intelligent Systems Development Steven Kuo Brian Lin.
Knowledge Representation of Statistic Domain For CBR Application Supervisor : Dr. Aslina Saad Dr. Mashitoh Hashim PM Dr. Nor Hasbiah Ubaidullah.
ES Model development Dr. Ahmed Elfaig The ES attempts to predict results from available information, data and knowledge The model should be able to infer.
TEMPERATURE MEASUREMENT & CONTROL DIVISION EVITHERM - WP 8 - Meeting 26 march 2003 Task 8.2 – CAP Compile database content of equipments and service providers.
COMM89 Knowledge-Based Systems Engineering Lecture 8 Life-cycles and Methodologies
Generic Tasks by Ihab M. Amer Graduate Student Computer Science Dept. AUC, Cairo, Egypt.
Metadata Common Vocabulary a journey from a glossary to an ontology of statistical metadata, and back Sérgio Bacelar
OHTO -01 SOFTWARE ENGINEERING LECTURE 3 Today: Requirements Analysis Requirements tell us what the system should do - not how it should do it.
Of 33 lecture 1: introduction. of 33 the semantic web vision today’s web (1) web content – for human consumption (no structural information) people search.
A View-based Methodology for Collaborative Ontology Engineering (VIMethCOE) Ernesto Jiménez Ruiz Rafael Berlanga Llavorí Temporal Knowledge Bases Group.
Intelligent Control Methods Lecture 7: Knowledge representation Slovak University of Technology Faculty of Material Science and Technology in Trnava.
Quality estimation of adaptive tutoring systems Volgograd State Technical University CAD department Pavel Vorobkalov Faculty advisor Shabalina O.A. Volgograd.
Use of Expert Systems for Application Systems Development.
Of An Expert System.  Introduction  What is AI?  Intelligent in Human & Machine? What is Expert System? How are Expert System used? Elements of ES.
Intelligent Control Methods Lecture 5: Expert Systems Slovak University of Technology Faculty of Material Science and Technology in Trnava.
Intelligent Control Methods Lecture 2: Artificial Intelligence Slovak University of Technology Faculty of Material Science and Technology in Trnava.
1 Pattern Recognition: Statistical and Neural Lonnie C. Ludeman Lecture 2 Nanjing University of Science & Technology.
16 April 2011 Alan, Edison, etc, Saturday.. Knowledge, Planning and Robotics 1.Knowledge 2.Types of knowledge 3.Representation of knowledge 4.Planning.
Faculty Economics & Business EBS 2033 Systems Development Lecture 1 The Systems Development Environment Lecturer: Puan Asleena Helmi.
Knowledge Representation Part I Ontology Jan Pettersen Nytun Knowledge Representation Part I, JPN, UiA1.
Design of Expert Systems
Intelligent Systems Development
Software Engineering Lecture 4 System Modeling The Analysis Stage.
Sukmawati NE PS ILKOM UNDIP
Entity Relationship (E-R) Modeling
Lee McCluskey University of Huddersfield
Architecture Components
Chapter 1 The Systems Development Environment
النظم الخبيرة Expert Systems (ES)
Intro to Expert Systems Paula Matuszek CSC 8750, Fall, 2004
Methontology: From Ontological art to Ontological Engineering
SCIENCE AND ENGINEERING PRACTICES
Qualitative Observation
Introduction To software engineering
TA : Mubarakah Otbi, Duaa al Ofi , Huda al Hakami
Ontology-Based Approaches to Data Integration
Presentation transcript:

Intelligent Control Methods Lecture 8: Knowledge Engineering Slovak University of Technology Faculty of Material Science and Technology in Trnava

2 Establishing of knowledge base The most important and most toilsome part of ES building. Knowledge sources:  experts  another sources: own knowledge of knowledge engineer textbooks annual reports, statistics books, reports, encyclopedias databases

3 Phases of knowledge base establishing: Similar to SW-waterfall model (planning, analysis, design, implementation, tests, operation, maintenance), adapted to knowledge engineering specifics. Identification Conceptualization Formalization Realization Tests

4 Identification participants identification problem identification sources estimation goals definition

5 Conceptualization data types estimation what is given, what should be derived relations, hierarchical relations knowledge types

6 Formalization Tasks, subtasks and characteristics from above mentioned phases are formalized into chosen knowledge representation schema

7 Realization Prototype of KB Tests KB supplementation by type tasks

8 Knowledge acquisition from experts Difficult. Experts are not able to formulate their inference.  Expertise-paradox: The more is the expert competent, so less is he able to describe his thinking. Expert must be not only asked, but also observed by task solutions. Expert used different glossary as knowledge engineer.

9 Expert knowledge Express- able expert knowledge Express- able expert knowledge, which is the KE able to understand Express- able expert knowledge, which is the KE able to formalize KB

10 Example for KB design Insurance company Damage compensation

11 Time consumption: Taskpeopletime (years) simple2 - 40,5 – 1,5 middle – 1,5 difficult

12 Tools for KB design and maintenance: Editors Complex systems for  KB filling  KB modification  KB supervision: syntactic checkout undefined objects redundant objects unmeetable conditions tautological conditions inaccessible objects type mismatch cyclic reference inconsistent weighting

Feedback: Privacy Policy Feedback
Do Not Sell My Personal Information
About project: SlidePlayer Terms of Service

© 2024 SlidePlayer.com Inc. All rights reserved.