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　　A Must Read for AI
　　本書為英文影印版，對應翻譯版：人工智能：一種現代的方法（第3版）（世界著名計算機教材精選）
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內容簡介

　　《大學計算機教育國外著名教材係列·人工智能：一種現代的方法（第3版）（影印版）》專業、經典的人工智能教材，已被全世界100多個國傢的1200多所大學用作教材。《大學計算機教育國外著名教材係列·人工智能：一種現代的方法（第3版）（影印版）》的全新版全麵而係統地介紹瞭人工智能的理論和實踐，闡述瞭人工智能領域的核心內容，並深入介紹瞭各個主要的研究方嚮。全書仍分為八大部分：一部分“人工智能”，第二部分“問題求解”，第三部分“知識與推理”，第四部分“規劃”，第五部分“不確定知識與推理”，第六部分“學習”，第七部分“通信、感知與行動”，第八部分“結論”。《大學計算機教育國外著名教材係列·人工智能：一種現代的方法（第3版）（影印版）》既詳細介紹瞭人工智能的基本概念、思想和算法，還描述瞭其各個研究方嚮前沿的進展，同時收集整理瞭詳實的曆史文獻與事件。另外，《大學計算機教育國外著名教材係列·人工智能：一種現代的方法（第3版）（影印版）》的配套網址為教師和學生提供瞭大量教學和學習資料。
　　《大學計算機教育國外著名教材係列·人工智能：一種現代的方法（第3版）（影印版）》適閤於不同層次和領域的研究人員及學生，是高等院校本科生和研究生人工智能課的優選教材，也是相關領域的科研與工程技術人員的重要參考書。

作者簡介

　　Stuart Russell，1962年生於英格蘭的Portsmouth。他於1982年以一等成績在牛津大學獲得物理學學士學位，並於1986年在斯坦福大學獲得計算機科學的博士學位。之後他進入加州大學伯剋利分校，任計算機科學教授，智能係統中心主任，擁有Smith-Zadeh工程學講座教授頭銜。1990年他獲得國傢科學基金的“總統青年研究者奬”（Presidential Young Investigator Award），1995年他是“計算機與思維奬”（Computer and Thought Award）的獲得者之一。1996年他是加州大學的Miller教授（Miller Professor），並於2000年被任命為首席講座教授（Chancellor's Professorship）。1998年他在斯坦福大學做過Forsythe紀念演講（Forsythe Memorial Lecture）。他是美國人工智能學會的會士和前執行委員會委員。他已經發錶100多篇論文，主題廣泛涉及人工智能領域。他的其他著作包括《在類比與歸納中使用知識》（The Use of Knowledge in Analogy abd Induction）．以及（與Eric Wefald閤著的）《做正確的事情：有限理性的研究》（Do the Right Thing: Studies in Limited Rationality）。

　　Peter Norvig，現為Google研究院主管（Director of Research），2002-2005年為負責核心Web搜索算法的主管。他是美國人工智能學會的會士和ACM的會士。他曾經是NASAAmes研究中心計算科學部的主任，負責NASA在人工智能和機器人學領域的研究與開發，他作為Junglee的首席科學傢幫助開發瞭一種zui早的互聯網信息抽取服務。他在布朗（ Brown）大學得應用數學學士學位，在加州大學伯剋利分校獲得計算機科學的博士學位。他獲得瞭伯剋利“卓越校友和工程創新奬”，從NASA獲得瞭“非凡成就勛章”。他曾任南加州大學的教授，並是伯剋利的研究員。他的其他著作包括《人工智能程序設計範型：通用Lisp語言的案例研究》（Paradigms of AI Programming: Case Studies in Common Lisp）和《Verbmobil：一個麵對麵對話的翻譯係統》（Verbmobil:A Translation System for Face-to-FaceDialog），以及《UNIX的智能幫助係統》（lntelligent Help Systemsfor UNIX）。

目錄

Ⅰ artificial intelligence
1 introduction
1.1what is al?
1.2the foundations of artificial intelligence
1.3the history of artificial intelligence
1.4the state of the art
1.5summary, bibliographical and historical notes, exercises
2 intelligent agents
2.1agents and environments
2.2good behavior: the concept of rationality
2.3the nature of environments
2.4the structure of agents
2.5summary, bibliographical and historical notes, exercises
Ⅱ problem-solving
3 solving problems by searching
3.1problem-solving agents
3.2example problems
3.3searching for solutions
3.4uninformed search strategies
3.5informed (heuristic) search strategies
3.6heuristic functions
3.7summary, bibliographical and historical notes, exercises
4 beyond classical search
4.1local search algorithms and optimization problems
4.2local search in continuous spaces
4.3searching with nondeterministic actions
4.4searching with partial observations
4.5online search agents and unknown environments
4.6summary, bibliographical and historical notes, exercises
5 adversarial search
5.1games
5.2optimal decisions in games
5.3alpha-beta pruning
5.4imperfect real-time decisions
5.5stochastic games
5.6partially observable games
5.7state-of-the-art game programs
5.8alternative approaches
5.9summary, bibliographical and historical notes, exercises
6 constraint satisfaction problems
6.1defining constraint satisfaction problems
6.2constraint propagation: inference in csps
6.3backtracking search for csps
6.4local search for csps
6.5the structure of problems
6.6summary, bibliographical and historical notes, exercises
Ⅲ knowledge, reasoning, and planning
7 logical agents
7.1knowledge-based agents
7.2the wumpus world
7.3logic
7.4propositional logic: a very simple logic
7.5propositional theorem proving
7.6effective propositional model checking
7.7agents based on propositional logic
7.8summary, bibliographical and historical notes, exercises
8 first-order logic
8.1representation revisited
8.2syntax and semantics of first-order logic
8.3using first-order logic
8.4knowledge engineering in first-order logic
8.5summary, bibliographical and historical notes, exercises
9 inference in first-order logic
9.1propositional vs. first-order inference
9.2unification and lifting
9.3forward chaining
9.4backward chaining
9.5resolution
9.6summary, bibliographical and historical notes, exercises
10 classical planning
10.1 definition of classical planning
10.2 algorithms for planning as state-space search
10.3 planning graphs
10.4 other classical planning approaches
10.5 analysis of planning approaches
10.6 summary, bibliographical and historical notes, exercises
11 planning and acting in the real world
11.1 time, schedules, and resources
11.2 hierarchical planning
11.3 planning and acting in nondeterministic domains
11.4 multiagent planning
11.5 summary, bibliographical and historical notes, exercises
12 knowledge representation
12.1 ontological engineering
12.2 categories and objects
12.3 events
12.4 mental events and mental objects
12.5 reasoning systems for categories
12.6 reasoning with default information
12.7 the intemet shopping world
12.8 summary, bibliographical and historical notes, exercises
Ⅳ uncertain knowledge and reasoning
13 quantifying uncertainty
13.1 acting under uncertainty
13.2 basic probability notation
13.3 inference using full joint distributions
13.4 independence
13.5 bayes' rule and its use
13.6 the wumpus world revisited
13.7 summary, bibliographical and historical notes, exercises
14 probabilistic reasoning
14.1 representing knowledge in an uncertain domain
14.2 the semantics of bayesian networks
14.3 efficient representation of conditional distributions
14.4 exact inference in bayesian networks
14.5 approximate inference in bayesian networks
14.6 relational and first-order probability models
14.7 other approaches to uncertain reasoning
14.8 summary, bibliographical and historical notes, exercises
15 probabilistic reasoning over time
15.1 time and uncertainty
15.2 inference in temporal models
15.3 hidden markov models
15.4 kalman filters
15.5 dynamic bayesian networks
15.6 keeping track of many objects
15.7 summary, bibliographical and historical notes, exercises
16 making simple decisions
16.1 combining beliefs and desires under uncertainty
16.2 the basis of utility theory
16.3 utility functions
16.4 multiattribute utility functions
16.5 decision networks
16.6 the value of information
16.7 decision-theoretic expert systems
16.8 summary, bibliographical and historical notes, exercises
17 making complex decisions
17.1 sequential decision problems
17.2 value iteration
17.3 policy iteration
17.4 partially observable mdps
17.5 decisions with multiple agents: game theory
17.6 mechanism design
17.7 summary, bibliographical and historical notes, exercises
V learning
18 learning from examples
18.1 forms of learning
18.2 supervised learning
18.3 leaming decision trees
18.4 evaluating and choosing the best hypothesis
18.5 the theory of learning
18.6 regression and classification with linear models
18.7 artificial neural networks
18.8 nonparametric models
18.9 support vector machines
18.10 ensemble learning
18.11 practical machine learning
18.12 summary, bibliographical and historical notes, exercises
19 knowledge in learning
19.1 a logical formulation of learning
19.2 knowledge in learning
19.3 explanation-based learning
19.4 learning using relevance information
19.5 inductive logic programming
19.6 summary, bibliographical and historical notes, exercis
20 learning probabilistic models
20.1 statistical learning
20.2 learning with complete data
20.3 learning with hidden variables: the em algorithm.
20.4 summary, bibliographical and historical notes, exercis
21 reinforcement learning
21. l introduction
21.2 passive reinforcement learning
21.3 active reinforcement learning
21.4 generalization in reinforcement learning
21.5 policy search
21.6 applications of reinforcement learning
21.7 summary, bibliographical and historical notes, exercis
VI communicating, perceiving, and acting
22 natural language processing
人工智能：一種現代的方法（第3版 影印版） [Artificial Intelligence:A Modern Approach (3rd Edition)] 下載 mobi epub pdf txt 電子書

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