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包郵 人工智能-英文版
所屬叢書(shū)：
國(guó)外計(jì)算機(jī)科學(xué)教材系列
開(kāi)本：
16開(kāi)
頁(yè)數(shù)：
370
本類(lèi)榜單：計(jì)算機(jī)/網(wǎng)絡(luò)銷(xiāo)量榜
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人工智能-英文版 版權(quán)信息
- ISBN：9787121086564
- 條形碼：9787121086564 ; 978-7-121-08656-4
- 裝幀：一般膠版紙
- 冊(cè)數(shù)：暫無(wú)
- 重量：暫無(wú)
- 所屬分類(lèi)：>
人工智能-英文版 本書(shū)特色
本書(shū)為廣大學(xué)生和人工智能開(kāi)發(fā)人員提供了學(xué)習(xí)人工智能相關(guān)概念的一種新思路。
書(shū)中包含人工智能在多個(gè)領(lǐng)域的許多*新應(yīng)用，這些領(lǐng)域包括游戲程序設(shè)計(jì)、群體智能、智能Agent、神經(jīng)網(wǎng)絡(luò)、人工免疫系統(tǒng)、遺傳算法、模式識(shí)別、數(shù)值優(yōu)化以及數(shù)據(jù)挖掘等。本書(shū)還討論了從早期的LISP語(yǔ)言到近期的Python語(yǔ)言等多種人工智能語(yǔ)言。書(shū)中不僅包括了人工智能的理論和主要課題，還介紹了從數(shù)據(jù)輸入到轉(zhuǎn)換再到數(shù)據(jù)輸出（即算法的使用）的實(shí)際需要的信息。因?yàn)閭鹘y(tǒng)的人工智能概念目前僅僅表示算法的各種類(lèi)型，因此需要用一種不同的方法來(lái)介紹人工智能算法。這種“傳感器－算法－效應(yīng)器”的方法為這些算法提供了一個(gè)基礎(chǔ)環(huán)境，能夠幫助學(xué)生和人工智能從業(yè)者更好地理解它們，從而更好地應(yīng)用這些算法。
人工智能-英文版 內(nèi)容簡(jiǎn)介
本書(shū)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*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。本書(shū)給出了算法的較詳細(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，本書(shū)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。
本書(shū)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，本書(shū)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。
人工智能-英文版 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 8 Neural 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
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 8 Neural 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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