Artificial Intelligence: A Guide to Intelligent Systems (4Ed.) | 誠品線上

人工智慧: 智慧型系統導論

內容簡介

內容簡介 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.

作者介紹

作者介紹 作 者：Michael Negnevitsky

產品目錄

產品目錄 TABLE OF CONTENTS1. Introduction to Intelligent Systems1.1 Intelligent Machines, or What Machines Can Do1.2 The History of Artificial Intelligence, or From the ‘Dark Ages’ to Knowledge-based Systems1.3 Generative AI1.4 SummaryQuestions for ReviewReferences2. Expert Systems2.1 Introduction, or Knowledge Representation Using Rules2.2 The Main Players in the Expert System Development Team2.3 Structure of a Rule-based Expert System2.4 Fundamental characteristics of an expert system2.5 Forward Chaining and Backward Chaining Inference Techniques2.6 MEDIA ADVISOR: A Demonstration Rule-based Expert System2.7 Conflict Resolution2.8 Uncertainty Management in Rule-based Expert Systems2.9 Advantages and Disadvantages of Rule-based Expert systems2.10 SummaryQuestions for ReviewReferences3. Fuzzy Systems3.1 Introduction, or What Is Fuzzy Thinking?3.2 Fuzzy Sets3.3 Linguistic Variables and Hedges3.4 Operations of Fuzzy Sets3.6 Fuzzy Inference3.7 Building a Fuzzy Expert System3.8 SummaryQuestions for ReviewReferences4. Frame-based Systems and Semantic Networks4.1 Introduction, or What Is a Frame?4.2 Frames as a Knowledge Representation Technique4.3 Inheritance in Frame-based Systems4.4 Methods and Demons4.5 Interaction of Frames and Rules4.6 Buy Smart: A Frame-based Expert System4.7 The Web of Data4.8 RDF – Resource Description Framework and RDF Triples4.9 Turtle, RDF Schema and OWL4.10 Querying the Semantic Web with SPARQL4.11 SummaryQuestions for ReviewReferences5. Artificial Neural Networks5.1 Introduction, or How the Brain Works5.2 The Neuron as a Simple Computing Element5.3 The Perceptron5.4 Multilayer Neural Networks5.5 Accelerated Learning in Multilayer Neural Networks5.6 The Hopfield Network5.7 Bidirectional Associative Memory5.8 Self-organising Neural Networks5.9 Reinforcement Learning5.10 SummaryQuestions for ReviewReferences6. Deep Learning and Convolutional Neural Networks6.1 Introduction, or How “Deep” Is a Deep Neural Network?6.2 Image Recognition or How Machines See the World6.3 Convolution in Machine Learning6.4 Activation Functions in Deep Neural Networks6.5 Convolutional Neural Networks6.6 Back-propagation Learning in Convolutional Networks6.7 Batch Normalisation6.8 SummaryQuestions for ReviewReferences7. Evolutionary Computation7.1 Introduction, or Can Evolution Be Intelligent?7.2 Simulation of Natural Evolution7.3 Genetic Algorithms7.4 Why Genetic Algorithms Work7.5 Maintenance Scheduling with Genetic Algorithms7.6 Genetic Programming7.7 Evolution Strategies7.8 Ant Colony Optimisation7.9 Particle Swarm Optimisation7.10 SummaryQuestions for ReviewReferences8. Hybrid Intelligent Systems8.1 Introduction, or How to Combine German Mechanics with Italian Love8.2 Neural Expert Systems8.3 Neuro-Fuzzy Systems8.4 ANFIS: Adaptive Neuro-Fuzzy Inference System8.5 Evolutionary Neural Networks8.6 Fuzzy Evolutionary Systems8.7 SummaryQuestions for ReviewReferences9. Knowledge Engineering9.1 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 SummaryQuestions for ReviewReferences10. Data Mining and Knowledge Discovery10.1 Introduction, or What Is Data Mining?10.2 Statistical Methods and Data Visualisation10.3 Principal Components Analysis10.4 Relational Databases and Database Queries10.5 The Data Warehouse and Multidimensional Data Analysis10.6 Decision Trees10.7 Association Rules and Market Basket Analysis10.8 SummaryQuestions for ReviewReferencesGlossaryIndex

商品規格

最佳賣點

最佳賣點 : 1.本書避開艱深的電腦科學專業術語，概念簡單直觀，不會充斥複雜的矩陣代數和微分方程。
2.不需要具備程式設計能力或深厚的微積分基礎就能理解內容。
3.介紹最新的AI工具和技術，包括MATLAB工具箱以及ChatGPT等，讀者可以靈活運用不同的工具來實作。

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