人工智能

內(nèi)容概要

人工智能經(jīng)常被人們認為是計算機科學(xué)中的一門高度復(fù)雜甚至令人生畏的學(xué)科。長期以來人工智能方面的書籍往往包含復(fù)雜矩陣代數(shù)和微分方程。本書形成于作者多年來給沒有多少微積分知識的學(xué)生授課時所用的講義，它假定讀者預(yù)先沒有編程的經(jīng)驗，并說明了智能系統(tǒng)中的大部分基礎(chǔ)知識實際上是簡單易懂的。本書目前已經(jīng)被國際上多所大學(xué)（例如，德國的馬德堡大學(xué)、日本的廣島大學(xué)、美國的波士頓大學(xué)和羅切斯特理工學(xué)院）采用。   如果你正在尋找關(guān)于人工智能或智能系統(tǒng)設(shè)計課程的淺顯易懂的入門級教材，如果你不是計算機科學(xué)領(lǐng)域的專業(yè)人員，而又正在尋找介紹基于知識系統(tǒng)最新技術(shù)發(fā)展的自學(xué)指南，本書將是最佳選擇。本書的主要內(nèi)容：      基于規(guī)則的專家系統(tǒng)     模糊專家系統(tǒng)     基于框架的專家系統(tǒng)     人工神經(jīng)網(wǎng)絡(luò)     進化計算     混合智能系統(tǒng)     知識工程     數(shù)據(jù)挖掘。

作者簡介

Michael negnevitsky 澳大利亞塔斯馬尼亞大學(xué)電氣工程和計算機科學(xué)系教授，他的許多研究課題都涉及人工智能和軟計算，一直致力于電氣工程，過程控制和環(huán)境工程中的、智能系統(tǒng)的開發(fā)和應(yīng)用，他著有200多篇論文、兩本書，并獲得了四項發(fā)明專利。

書籍目錄

PrefacePreface to the Second editionAcknowledgements1 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‘DarkAges’To Knowledge-Based Systems  1.3 Summary      Questions For Review      References2 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        References3 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    References4 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    Bibliography5 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    Bibliography6 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    References7 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    References8 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    References9 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    ReferencesGlossaryAppendixIndex

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