人工智能

前言

　　The main objective of the book remains the same as in the first edition - to provide the reader with practical understanding of the field of computer intelligence. It is intended as an introductory text suitable for a one-semester course， and assumes the students have only limited knowledge of calculus and little or no programming experience.　　In terms of the coverage， this edition introduces a new chapter on data mining and demonstrates several new applications of intelligent tools for solving complex real-world problems. The major changes are as follows:　　In the new chapter， 'Data mining and knowledge discovery'， we introduce　　data mining as an integral part of knowledge discovery in large databases. We consider the main techniques and tools for turning data into knowledge， including statistical methods， data visualisation tools， Structured Query Language， decision trees and market basket analysis. We also present several case studies on data mining applications.　　In Chapter 9， we add a new case study on clustering with a self-organising neural network.　　Finally， we have expanded the book's references and bibliographies， and updated the list of AI tools and vendors in the appendix.　　Michael Negnevitsky　　Hobart， Tasmania， Australia　　September 2010

內(nèi)容概要

人工智能經(jīng)常被人們認(rèn)為是計(jì)算機(jī)科學(xué)中一門高度復(fù)雜甚至令人生畏的學(xué)科。長(zhǎng)期以來(lái)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。本書基于作者多年來(lái)給沒(méi)有多少微積分知識(shí)的學(xué)生授課時(shí)所用的講義。假定讀者沒(méi)有編程經(jīng)驗(yàn)，以簡(jiǎn)單易懂的方式介紹了智能系統(tǒng)的基礎(chǔ)知識(shí)。
尼格尼維斯基編著的《人工智能》目前已經(jīng)被國(guó)際上多所大學(xué)（例如，德國(guó)的馬格德堡大學(xué)、日本的廣島大學(xué)、美國(guó)的波士頓大學(xué)和羅切斯特理工學(xué)院等）采納為教材。
如果您正在尋找關(guān)于人工智能或智能系統(tǒng)設(shè)計(jì)課程的淺顯易懂的入門級(jí)教材，如果您不是計(jì)算機(jī)科學(xué)領(lǐng)域的專業(yè)人員而又正在尋找介紹基于知識(shí)系統(tǒng)最新技術(shù)發(fā)展的自學(xué)指南，本書將是您的最佳選擇。
與上一版相比，本版進(jìn)行了全面更新，以反映人工智能領(lǐng)域的最新進(jìn)展。其中新增了數(shù)據(jù)挖掘與知識(shí)發(fā)現(xiàn)一章和自組織神經(jīng)網(wǎng)絡(luò)聚類一節(jié)內(nèi)容。同時(shí)補(bǔ)充了4個(gè)新的案例研究。

作者簡(jiǎn)介

澳大利亞塔斯馬尼亞大學(xué)電氣工程和計(jì)算機(jī)科學(xué)系教授。他的許多研究課題都涉及人工智能和軟計(jì)算。他一直致力于電氣工程、過(guò)程控制和環(huán)境工程中智能系統(tǒng)的開(kāi)發(fā)和應(yīng)用，發(fā)表了300多篇論文，著有2本專著，并獲得了4項(xiàng)發(fā)明專利。

書籍目錄

Preface
Preface to the third edition
Overview of the book
Acknowledgements
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 method
  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  Inheritance in frame-based systems
  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 memory
  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  Evolution strategies
  7.7  Genetic programming
  7.8  Summary
  Questions for review
  References
  Bibliography
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 genetic algorithms work for my problem?
  9.6  Will a hybrid intelligent system work for my problem?
  9.7  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 component 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
Appendix: AI tools and vendors
index

章節(jié)摘錄

版權(quán)頁(yè)：插圖：The first work recognised in the field of artificial intelligence （AI） was presentedby Warren McCulloch and Walter Pitts in 1943. McCulloch had degrees inphilosophy and medicine from Columbia University and became the Director ofthe Basic Research Laboratory in the Department of Psychiatry at the Universityof Illinois. His research on the central nervous system resulted in the first majorcontribution to AI: a model of neurons of the brain.  McCulloch and his co-author Walter Pitts, a young mathematician, proposeda model of artificial neural networks in which each neuron was postulated asbeing in a binary state: that is, in either an on or off condition （McCulloch andPitts, 1943）. They demonstrated that their neural network model was, in fact,equivalent to the Turing machine, and proved that any computable functioncould be computed by some network of connected neurons. McCulloch and Pittsalso showed that simple network structures could learn.

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