Artificial Intelligence: A Guide to Intelligent Systems, 4/e (Paperback)
暫譯: 人工智慧：智能系統指南（第4版）

Michael Negnevitsky

Artificial Intelligence: A Guide to Intelligent Systems, 4/e (Paperback)
  • 出版商: Pearson FT Press
  • 出版日期: 2024-09-24
  • 定價: $1,680
  • 售價: 9.5 折 $1,596
  • 語言: 英文
  • 頁數: 600
  • ISBN: 1292730854
  • ISBN-13: 9781292730851
  • 相關分類: 人工智慧

    • 立即出貨 (庫存 < 3)

相關主題

商品描述

What are the principles behind intelligent systems? How are they built? What are intelligent systems useful for? How do we choose the right tool for the job? These questions are answered by Michael Negnevitsky’s Artificial Intelligence: A Guide to Intelligent Systems.

Unlike many books on computer intelligence, which use complex computer science terminology and are crowded with complex matrix algebra and differential equations, this text demonstrates that the ideas behind intelligent systems are simple and straightforward. This text assumes little or no programming experience as it tackles topics like expert systems, fuzzy systems, artificial neural networks, evolutionary computation, knowledge engineering, and data mining.

商品描述(中文翻譯)

智能系統背後的原則是什麼？它們是如何構建的？智能系統有什麼用途？我們如何選擇合適的工具來完成任務？這些問題在 Michael Negnevitsky 的《人工智慧：智能系統指南》中得到了回答。

與許多關於計算機智能的書籍不同，這些書籍使用複雜的計算機科學術語，並充斥著複雜的矩陣代數和微分方程，這本書展示了智能系統背後的理念是簡單明瞭的。這本書假設讀者幾乎沒有或完全沒有程式設計經驗，涵蓋了專家系統、模糊系統、人工神經網絡、進化計算、知識工程和數據挖掘等主題。

目錄大綱

TABLE OF CONTENTS
1. Introduction to 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 Generative AI
 1.4 Summary
 Questions for Review
 References

2. Expert Systems
 2.1 Introduction, or Knowledge Representation Using Rules
 2.2 The Main Players in the Expert System Development Team
 2.3 Structure of a Rule-based Expert System
 2.4 Fundamental characteristics of an expert system
 2.5 Forward Chaining and Backward Chaining Inference Techniques
 2.6 MEDIA ADVISOR: A Demonstration Rule-based Expert System
 2.7 Conflict Resolution
 2.8 Uncertainty Management in Rule-based Expert Systems
 2.9 Advantages and Disadvantages of Rule-based Expert systems
 2.10 Summary
 Questions for Review
 References

3. Fuzzy Systems
 3.1 Introduction, or What Is Fuzzy Thinking?
 3.2 Fuzzy Sets
 3.3 Linguistic Variables and Hedges
 3.4 Operations of Fuzzy Sets
 3.6 Fuzzy Inference
 3.7 Building a Fuzzy Expert System
 3.8 Summary
 Questions for Review
 References

4. Frame-based Systems and Semantic Networks
 4.1 Introduction, or What Is a Frame?
 4.2 Frames as a Knowledge Representation Technique
 4.3 Inheritance in Frame-based Systems
 4.4 Methods and Demons
 4.5 Interaction of Frames and Rules
 4.6 Buy Smart: A Frame-based Expert System
 4.7 The Web of Data
 4.8 RDF – Resource Description Framework and RDF Triples
 4.9 Turtle, RDF Schema and OWL
 4.10 Querying the Semantic Web with SPARQL
 4.11 Summary
 Questions for Review
 References

5. Artificial Neural Networks
 5.1 Introduction, or How the Brain Works
 5.2 The Neuron as a Simple Computing Element
 5.3 The Perceptron
 5.4 Multilayer Neural Networks
 5.5 Accelerated Learning in Multilayer Neural Networks
 5.6 The Hopfield Network
 5.7 Bidirectional Associative Memory
 5.8 Self-organising Neural Networks
 5.9 Reinforcement Learning
 5.10 Summary
 Questions for Review
 References

6. Deep Learning and Convolutional Neural Networks
 6.1 Introduction, or How “Deep” Is a Deep Neural Network?
 6.2 Image Recognition or How Machines See the World
 6.3 Convolution in Machine Learning
 6.4 Activation Functions in Deep Neural Networks
 6.5 Convolutional Neural Networks
 6.6 Back-propagation Learning in Convolutional Networks
 6.7 Batch Normalisation
 6.8 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 Maintenance Scheduling with Genetic Algorithms
 7.6 Genetic Programming
 7.7 Evolution Strategies
 7.8 Ant Colony Optimisation
 7.9 Particle Swarm Optimisation
 7.10 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-Fuzzy Inference System
 8.5 Evolutionary Neural Networks
 8.6 Fuzzy Evolutionary Systems
 8.7 Summary
 Questions for Review
 References

9. Knowledge Engineering
 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 a Deep Neural Network Work for My Problem?
 9.6 Will Genetic Algorithms Work for My Problem?
 9.7 Will Particle Swarm Optimisation Work for My Problem?
 9.8 Will a Hybrid Intelligent System Work for My Problem?
 9.9 Summary
 Questions for Review
 References

10. Data Mining and Knowledge Discovery
 10.1 Introduction, or What Is Data Mining?
 10.2 Statistical Methods and Data Visualisation
 10.3 Principal Components Analysis
 10.4 Relational Databases and Database Queries
 10.5 The Data Warehouse and Multidimensional Data Analysis
 10.6 Decision Trees
 10.7 Association Rules and Market Basket Analysis
 10.8 Summary
 Questions for Review
 References

Glossary

Index

目錄大綱(中文翻譯)

TABLE OF CONTENTS

1. Introduction to 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 Generative AI

 1.4 Summary

 Questions for Review

 References

2. Expert Systems

 2.1 Introduction, or Knowledge Representation Using Rules

 2.2 The Main Players in the Expert System Development Team

 2.3 Structure of a Rule-based Expert System

 2.4 Fundamental characteristics of an expert system

 2.5 Forward Chaining and Backward Chaining Inference Techniques

 2.6 MEDIA ADVISOR: A Demonstration Rule-based Expert System

 2.7 Conflict Resolution

 2.8 Uncertainty Management in Rule-based Expert Systems

 2.9 Advantages and Disadvantages of Rule-based Expert systems

 2.10 Summary

 Questions for Review

 References

3. Fuzzy Systems

 3.1 Introduction, or What Is Fuzzy Thinking?

 3.2 Fuzzy Sets

 3.3 Linguistic Variables and Hedges

 3.4 Operations of Fuzzy Sets

 3.6 Fuzzy Inference

 3.7 Building a Fuzzy Expert System

 3.8 Summary

 Questions for Review

 References

4. Frame-based Systems and Semantic Networks

 4.1 Introduction, or What Is a Frame?

 4.2 Frames as a Knowledge Representation Technique

 4.3 Inheritance in Frame-based Systems

 4.4 Methods and Demons

 4.5 Interaction of Frames and Rules

 4.6 Buy Smart: A Frame-based Expert System

 4.7 The Web of Data

 4.8 RDF – Resource Description Framework and RDF Triples

 4.9 Turtle, RDF Schema and OWL

 4.10 Querying the Semantic Web with SPARQL

 4.11 Summary

 Questions for Review

 References

5. Artificial Neural Networks

 5.1 Introduction, or How the Brain Works

 5.2 The Neuron as a Simple Computing Element

 5.3 The Perceptron

 5.4 Multilayer Neural Networks

 5.5 Accelerated Learning in Multilayer Neural Networks

 5.6 The Hopfield Network

 5.7 Bidirectional Associative Memory

 5.8 Self-organising Neural Networks

 5.9 Reinforcement Learning

 5.10 Summary

 Questions for Review

 References

6. Deep Learning and Convolutional Neural Networks

 6.1 Introduction, or How “Deep” Is a Deep Neural Network?

 6.2 Image Recognition or How Machines See the World

 6.3 Convolution in Machine Learning

 6.4 Activation Functions in Deep Neural Networks

 6.5 Convolutional Neural Networks

 6.6 Back-propagation Learning in Convolutional Networks

 6.7 Batch Normalisation

 6.8 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 Maintenance Scheduling with Genetic Algorithms

 7.6 Genetic Programming

 7.7 Evolution Strategies

 7.8 Ant Colony Optimisation

 7.9 Particle Swarm Optimisation

 7.10 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-Fuzzy Inference System

 8.5 Evolutionary Neural Networks

 8.6 Fuzzy Evolutionary Systems

 8.7 Summary

 Questions for Review

 References

9. Knowledge Engineering

 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 a Deep Neural Network Work for My Problem?

 9.6 Will Genetic Algorithms Work for My Problem?

 9.7 Will Particle Swarm Optimisation Work for My Problem?

 9.8 Will a Hybrid Intelligent System Work for My Problem?

 9.9 Summary

 Questions for Review

 References

10. Data Mining and Knowledge Discovery

 10.1 Introduction, or What Is Data Mining?

 10.2 Statistical Methods and Data Visualisation

 10.3 Principal Components Analysis

 10.4 Relational Databases and Database Queries

 10.5 The Data Warehouse and Multidimensional Data Analysis

 10.6 Decision Trees

 10.7 Association Rules and Market Basket Analysis

 10.8 Summary

 Questions for Review

 References

Glossary

Index

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