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Artificial Intelligence : A Guide to Intelligent Systems by Michael Negnevitsky (2004, Hardcover, Revised edition)

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This title shows students how to build intelligent systems drawing on techniques from knowledge-based systems, neural networks, fuzzy systems, evolutionary computation and now also intelligent agents. The principles behind these techniques are explained without resorting to complex mathematics.

Product Identifiers

Publisher
Pearson Education
ISBN-10
0321204662
ISBN-13
9780321204660
eBay Product ID (ePID)
30787460

Product Key Features

Author
Michael Negnevitsky
Publication Name
Artificial Intelligence : a Guide to Intelligent Systems
Format
Hardcover
Language
English
Features
Revised
Subject
Expert Systems, Intelligence (Ai) & Semantics
Publication Year
2004
Type
Textbook
Subject Area
Computers
Number of Pages
440 Pages

Dimensions

Item Length
6.5 in
Item Height
1.2 in
Item Width
9.4 in
Item Weight
28.4 Oz

Additional Product Features

Edition Number
2
LCCN
2004-051817
Lc Classification Number
Qa76.76.E95n445 2004
Edition Description
Revised Edition
Table of Content
1 Introduction To Knowledge-Based Intelligent Systems 1.1 Intelligent Machines, Or What Machines Can Do 1.2 The History Of Artificial Intelligence, Or From The 'Dark Ages' To Knowledge-Based Systems 1.3 Summary Questions For Review References 2 Rule-Based Expert Systems 2.1 Introduction, Or What Is Knowledge? 2.2 Rules As A Knowledge Representation Technique 2.3 The Main Players In The Expert System Development Team 2.4 Structure Of A Rule-Based Expert System 2.5 Fundamental Characteristics Of An Expert System 2.6 Forward Chaining And Backward Chaining Inference Techniques 2.7 MEDIA ADVISOR: A Demonstration Rule-Based Expert System 2.8 Conflict Resolution 2.9 Advantages And Disadvantages Of Rule-Based Expert Systems 2.10 Summary Questions For Review References 3 Uncertainty Management In Rule-Based Expert Systems 3.1 Introduction, Or What Is Uncertainty? 3.2 Basic Probability Theory 3.3 Bayesian Reasoning 3.4 FORECAST: Bayesian Accumulation Of Evidence 3.5 Bias Of The Bayesian Mesod 3.6 Certainty Factors Theory And Evidential Reasoning 3.7 FORECAST: An Application Of Certainty Factors 3.8 Comparison Of Bayesian Reasoning And Certainty Factors 3.9 Summary Questions For Review References 4 Fuzzy Expert Systems 4.1 Introduction, Or What Is Fuzzy Thinking? 4.2 Fuzzy Sets 4.3 Linguistic Variables And Hedges 4.4 Operations Of Fuzzy Sets 4.5 Fuzzy Rules 4.6 Fuzzy Inference 4.7 Building A Fuzzy Expert System 4.8 Summary Questions For Review References Bibliography 5 Frame-Based Expert Systems 5.1 Introduction, Or What Is A Frame? 5.2 Frames As A Knowledge Representation Technique 5.3 Inference In Frame-Based Experts 5.4 Methods And Demons 5.5 Interaction Of Frames And Rules 5.6 Buy Smart: A Frame-Based Expert System 5.7 Summary Questions For Review References Bibliography 6 Artificial Neural Networks 6.1 Introduction, Or How The Brain Works 6.2 The Neuron As A Simple Computing Element 6.3 The Perceptron 6.4 Multilayer Neural Networks 6.5 Accelerated Learning In Multilayer Neural Networks 6.6 The Hopfield Network 6.7 Bidirectional Associative Memories 6.8 Self-Organising Neural Networks 6.9 Summary Questions For Review References 7 Evolutionary Computation 7.1 Introduction, Or Can Evolution Be Intelligent? 7.2 Simulation Of Natural Evolution 7.3 Genetic Algorithms 7.4 Why Genetic Algorithms Work 7.5 Case Study: Maintenance Scheduling With Genetic Algorithms 7.6 Evolutionary Strategies 7.7 Genetic Programming 7.8 Summary Questions For Review References 8 Hybrid Intelligent Systems 8.1 Introduction, Or How To Combine German Mechanics With Italian Love 8.2 Neural Expert Systems 8.3 Neuro-Fuzzy Systems 8.4 ANFIS: Adaptive Neuro-Fuzy Inference System 8.5 Evolutionary Neural Networks 8.6 Fuzzy Evolutionary Systems 8.7 Summary Questions For Review References 9 Knowledge Engineering And Data Mining 9.1 Introduction, Or What Is Knowledge Engineering? 9.2 Will An Expert System Work For My Problem? 9.3 Will A Fuzzy Expert System Work For My Problem? 9.4 Will A Neural Network Work For My Problem? 9.5 Will Genetic Algorithms Work For My Problem? 9.6 Will A Neuro-Fuzzy System Work For My Problem? 9.7 Data Mining And Knowledge Discovery 9.8 Summary Questions For Review References Glossary Appendix Index
Copyright Date
2005
Target Audience
College Audience
Dewey Decimal
006.3
Dewey Edition
22
Illustrated
Yes

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Artificial Intelligence : A Guide to Intelligent Systems by Michael Negnevitsky (2004, Hardcover, Revised edition)

About this product

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All listings for this product

item 1 Artificial Intelligence: A Guide to Intelligent Systems [2nd Edition]

item 2 Artificial Intelligence: A Guide to Intelligent Systems [2nd Edition]

item 3 Artificial Intelligence : A Guide to Intelligent Systems Michael

item 4 Artificial Intelligence: A Guide to Intelligent Systems by Negnevitsky, Michael

item 5 Artificial Intelligence: A Guide to Intellig... by Negnevitsky, Dr Mich Hardback

item 6 Artificial Intelligence: A Guide to Intelligent Systems (2nd Edition) - GOOD

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Research Design : Qualitative, Quantitative, and Mixed Methods Approaches by J. David Creswell and John W. Creswell (2022, Trade Paperback)

PMBOK® Guide Ser.: Guide to the Project Management Body of Knowledge (PMBOK Guide) and the Standard for Project Management by Project Management Institute (2021, Trade Paperback, New Edition)

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