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人工智能(英文版)

人工智能(英文版)

定 價(jià):¥45.00

作 者: (美)瓊斯(Jones,M.T.) 著
出版社: 電子工業(yè)出版社
叢編項(xiàng): 國(guó)外計(jì)算機(jī)科學(xué)教材系列
標(biāo) 簽: 影印版

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ISBN: 9787121086564 出版時(shí)間: 2009-06-01 包裝: 平裝
開(kāi)本: 16開(kāi) 頁(yè)數(shù): 370 字?jǐn)?shù):  

內(nèi)容簡(jiǎn)介

  《人工智能(英文版)》包含當(dāng)前人工智能(AI)研究的主要內(nèi)容,尤其強(qiáng)調(diào)實(shí)際應(yīng)用,涉及數(shù)據(jù)挖掘等許多最新應(yīng)用領(lǐng)域。全書(shū)共13章,分別講述了AI的歷史、不用知識(shí)的搜索、用知識(shí)的搜索、AI與博弈、知識(shí)表示、機(jī)器學(xué)習(xí)、演化計(jì)算、神經(jīng)網(wǎng)絡(luò)I、機(jī)器人學(xué)與AI、智能Agent、來(lái)自生物的模型與混合模型以及AⅡ語(yǔ)言?!度斯ぶ悄埽ㄓ⑽陌妫方o出了算法的較詳細(xì)實(shí)現(xiàn),與現(xiàn)有的以理論基礎(chǔ)為核心的大多數(shù)經(jīng)典人工智能著作相比,《人工智能(英文版)》有自身的鮮明特色,且內(nèi)容與國(guó)內(nèi)人工智能課程的教學(xué)內(nèi)容吻合,尤其有利于培養(yǎng)學(xué)生解決人工智能實(shí)際問(wèn)題的能力。 《人工智能(英文版)》適合高等學(xué)校計(jì)算機(jī)、自動(dòng)化等信息學(xué)科的本科生和研究生閱讀,也適合廣大人工智能愛(ài)好者自學(xué)使用,《人工智能(英文版)》也能為人工智能研究人員了解各種算法的設(shè)計(jì)思路和具體實(shí)現(xiàn)框架提供參考。

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圖書(shū)目錄

Chapter 1 The History of AI
What is Intelligence?
The Search for Mechanical Intelligence
The Very Early Days (the early 1950s)
Artificial Intelligence Emerges as a Field
AI's Winter
AI Re-emerges
AI Inter-Disciplinary R&D
Systems Approach
Overview of this Book
Chapter Summary
References
Resources
Exercises
Chapter 2 Uninformed Search
Search and AI
Classes of Search
General State Space Search
Trees, Graphs, and Representation
Uninformed Search
Improvements
Algorithm Advantages
Chapter Summary
Algorithms Summary
References
Exercises
Chapter 3 Informed Search
Informed Search
Best-First Search (Best-FS)
A* Search
Hill-Climbing Search
Simulated Annealing (SA)
Tabu Search
Constraint Satisfaction Problems (CSP)
Constraint Satisfaction Algorithms
Chapter Summary
Algorithms Summary
References
Resources
Exercises
Chapter 4 AI and Games
Two-Player Games
The Minimax Algorithm
Classical Game AI
Video Game AI
Chapter Summary
References
Resources
Exercises
Chapter 5 Knowledge Representation
Introduction
Types of Knowledge
The Role of Knowledge
Semantic Networks
Frames
Propositional Logic
First-Order Logic (Predicate Logic)
Semantic Web
Computational Knowledge Discovery
Ontology
Communication of Knowledge
Chapter Summary
References
Resources
Exercises
Chapter 6 Machine Learning
Machine-Learning Algorithms
Chapter Summary
Resources
Exercises
Chapter 7 Evolutionary Computation
Short History of Evolutionary Computation
Biological Motivation
……
Chapter 8Neural Networks I
Chapter 9 Neural Networks II
Chapter 10 Robotics and AI
Chapter 11 Intelligent Agents
Chapter 12 Biologically Inspired and Hybrid Models
Chapter 13 The Languages of AI

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