Fall 2015.  Types of Uncertainty 1. Randomness : Probability Knowledge about the relative frequency of each event in some domain Lack of knowledge which.

Slides:



Advertisements
Similar presentations
Slide 1 of 18 Uncertainty Representation and Reasoning with MEBN/PR-OWL Kathryn Blackmond Laskey Paulo C. G. da Costa The Volgenau School of Information.
Advertisements

Managing Knowledge in the Digital Firm (II) Soetam Rizky.
Affinity Set and Its Applications Moussa Larbani and Yuh-Wen Chen.
Rulebase Expert System and Uncertainty. Rule-based ES Rules as a knowledge representation technique Type of rules :- relation, recommendation, directive,
DETC06: Uncertainty Workshop; Evidence & Possibility Theories Evidence and Possibility Theories in Engineering Design Zissimos P. Mourelatos Mechanical.
Fuzzy Measures and Integrals
Fuzzy Sets and Applications Introduction Introduction Fuzzy Sets and Operations Fuzzy Sets and Operations.
AI TECHNIQUES Fuzzy Logic (Fuzzy System). Fuzzy Logic : An Idea.
Huge Raw Data Cleaning Data Condensation Dimensionality Reduction Data Wrapping/ Description Machine Learning Classification Clustering Rule Generation.
1 Slides for the book: Probabilistic Robotics Authors: Sebastian Thrun Wolfram Burgard Dieter Fox Publisher: MIT Press, Web site for the book & more.
Modeling Human Reasoning About Meta-Information Presented By: Scott Langevin Jingsong Wang.
Rough Sets Theory Speaker:Kun Hsiang.
WRSTA, 13 August, 2006 Rough Sets in Hybrid Intelligent Systems For Breast Cancer Detection By Aboul Ella Hassanien Cairo University, Faculty of Computer.
Fuzzy Logic Based on a system of non-digital (continuous & fuzzy without crisp boundaries) set theory and rules. Developed by Lotfi Zadeh in 1965 Its advantage.
CS 547: Sensing and Planning in Robotics Gaurav S. Sukhatme Computer Science Robotic Embedded Systems Laboratory University of Southern California
AI – CS364 Hybrid Intelligent Systems Overview of Hybrid Intelligent Systems 07 th November 2005 Dr Bogdan L. Vrusias
Fuzzy Medical Image Segmentation
Probabilistic Robotics Introduction Probabilities Bayes rule Bayes filters.
Lecture 05 Rule-based Uncertain Reasoning
Soft Computing 1 Neuro-Fuzzy and Soft Computing chapter 1 J.-S.R. Jang Bill Cheetham Kai Goebel.
COMP 578 Fuzzy Sets in Data Mining Keith C.C. Chan Department of Computing The Hong Kong Polytechnic University.
WELCOME TO THE WORLD OF FUZZY SYSTEMS. DEFINITION Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept.
Introduction to fuzzy logic ME Sem II G.Anuradha.
Fuzzy Systems and Applications
The Equivalence between fuzzy logic controllers and PD controllers for single input systems Professor: Chi-Jo Wang Student: Nguyen Thi Hoai Nam Student.
THE MODEL OF ASIS FOR PROCESS CONTROL APPLICATIONS P.Andreeva, T.Atanasova, J.Zaprianov Institute of Control and System Researches Topic Area: 12. Intelligent.
Fuzzy Logic. Lecture Outline Fuzzy Systems Fuzzy Sets Membership Functions Fuzzy Operators Fuzzy Set Characteristics Fuzziness and Probability.
Fuzzy Rules 1965 paper: “Fuzzy Sets” (Lotfi Zadeh) Apply natural language terms to a formal system of mathematical logic
10/6/2015 1Intelligent Systems and Soft Computing Lecture 0 What is Soft Computing.
Copyright © 2006, The McGraw-Hill Companies, Inc. All rights reserved. Decision Support Systems Chapter 10.
 Definition Definition  Bit of History Bit of History  Why Fuzzy Logic? Why Fuzzy Logic?  Applications Applications  Fuzzy Logic Operators Fuzzy.
Estimating Component Availability by Dempster-Shafer Belief Networks Estimating Component Availability by Dempster-Shafer Belief Networks Lan Guo Lane.
Uncertainty Management in Rule-based Expert Systems
“Principles of Soft Computing, 2 nd Edition” by S.N. Sivanandam & SN Deepa Copyright  2011 Wiley India Pvt. Ltd. All rights reserved. CHAPTER 12 FUZZY.
1 Modeling with uncertainty requires more than probability theory There are problems where boundaries are gradual EXAMPLES: What is the boundary of the.
Fuzzy Measures and Integrals 1. Fuzzy Measure 2. Belief and Plausibility Measure 3. Possibility and Necessity Measure 4. Sugeno Measure 5. Fuzzy Integrals.
CH751 퍼지시스템 특강 Uncertainties in Intelligent Systems 2004 년도 제 1 학기.
Chapter 13 Fuzzy Logic 1. Handling Uncertainty Probability-based approach and Bayesian theory Certainty factor and evidential reasoning Fuzzy logic 2.
PART 10 Pattern Recognition 1. Fuzzy clustering 2. Fuzzy pattern recognition 3. Fuzzy image processing FUZZY SETS AND FUZZY LOGIC Theory and Applications.
2008/9/15fuzzy set theory chap01.ppt1 Introduction to Fuzzy Set Theory.
AI Fuzzy Systems. History, State of the Art, and Future Development Sde Seminal Paper “Fuzzy Logic” by Prof. Lotfi Zadeh, Faculty in Electrical.
International Conference on Fuzzy Systems and Knowledge Discovery, p.p ,July 2011.
Chapter 1: Introduction to Neuro-Fuzzy (NF) and Soft Computing (SC)
Textbook Basics of an Expert System: – “Expert systems: Design and Development,” by: John Durkin, 1994, Chapters 1-4. Uncertainty (Probability, Certainty.
Data Mining By Farzana Forhad CS 157B. Agenda Decision Tree and ID3 Rough Set Theory Clustering.
Probabilistic Robotics Introduction Probabilities Bayes rule Bayes filters.
CHAPTER 1 1 INTRODUCTION “Principles of Soft Computing, 2 nd Edition” by S.N. Sivanandam & SN Deepa Copyright  2011 Wiley India Pvt. Ltd. All rights reserved.
Fuzzy C-means Clustering Dr. Bernard Chen University of Central Arkansas.
Introduction of Fuzzy Inference Systems By Kuentai Chen.
Probabilistic Robotics Probability Theory Basics Error Propagation Slides from Autonomous Robots (Siegwart and Nourbaksh), Chapter 5 Probabilistic Robotics.
Dealing with Uncertainty: A Survey of Theories and Practice Yiping Li, Jianwen Chen and Ling Feng IEEE Transactions on Knowledge and Data Engineering,
Chapter 13 (Continued) Fuzzy Expert Systems 1. Fuzzy Rule-based Expert System 2.
Inexact Reasoning 2 Session 10
CHAPTER 5 Handling Uncertainty BIC 3337 EXPERT SYSTEM.
Introduction to Fuzzy Logic and Fuzzy Systems
Artificial Intelligence CIS 342
Inexact Reasoning 2 Session 10
Quick Review Probability Theory
RESEARCH APPROACH.
Fuzzy Logic and Fuzzy Sets
Dr. Unnikrishnan P.C. Professor, EEE
Introduction to Fuzzy Theory
CSE-490DF Robotics Capstone
CH751 퍼지시스템 특강 Uncertainties in Intelligent Systems
Intelligent Systems and
Dr. Unnikrishnan P.C. Professor, EEE
28th September 2005 Dr Bogdan L. Vrusias
Fuzzy Logic Bai Xiao.
Fuzzy Logic Based on a system of non-digital (continuous & fuzzy without crisp boundaries) set theory and rules. Developed by Lotfi Zadeh in 1965 Its advantage.
basic probability and bayes' rule
Presentation transcript:

Fall 2015

 Types of Uncertainty 1. Randomness : Probability Knowledge about the relative frequency of each event in some domain Lack of knowledge which event will occur in next time 2. Incompleteness : Imputation by EM Lack of knowledge or insufficient data 3. Ambiguity : Dempster-Shafer’s Belief Theory for Evidential Reasoning Uncertainty due to the lack of evidence ex) “The criminal is left-handed or not”

 Types of Uncertainty (continued) 4. Imprecision : Ambiguity due to the lack of accuracy of observed data ex) Character Recognition 5. Fuzziness (vagueness) : Uncertainty due to the vagueness of boundary ex) Beautiful woman, Tall man

 Powerful tool for vagueness  Description of vague linguistic terms and algorithms  Operation on vague linguistic terms  Reasoning with vague linguistic rules  Representation of clusters with vague boundaries

History of Fuzzy Sets and Applications 1965 Zadeh Fuzzy Sets 1965 Zadeh Fuzzy Sets 1972 Sugeno Fuzzy Integrals 1972 Sugeno Fuzzy Integrals 1975 Zadeh 1975 Zadeh Fuzzy Algorithm & Approximate Reasoning Fuzzy Algorithm & Approximate Reasoning 1974 Mamdani Fuzzy Control 1974 Mamdani Fuzzy Control 1978 North Holland Fuzzy Sets and Systems 1978 North Holland Fuzzy Sets and Systems 1982 Bezdek Fuzzy C-Mean 1982 Bezdek Fuzzy C-Mean

Fuzzy Sets Applications Methods Fuzzy Measure Fuzzy Logic Fuzzy Integrals Fuzzy Measure Fuzzy Relation Fuzzy Numbers Extension Principle Fuzzy Optimization Linguistic Variable Fuzzy Algorithm Approximate Reasoning Foundation Clustering Pattern Recognition Data Processing Decision Making Evaluation Estimation Expert Systems Fuzzy Computer Fuzzy Control

Theory on fuzzy sets 1) fuzzy set 2) fuzzy number 3) fuzzy logic 4) fuzzy relation Applications 1) fuzzy database 2) fuzzy control and expert system 3) robotics 4) fuzzy computer 5) pattern recognition Rough Sets & Applications

 Examination 40% (1 exam)  Term Project40% (2-3 projects)  Presentation 20%