人工智能

出版时间：2002-4  出版社：人民邮电  作者：[英]拉塞尔，[美]诺文 著  页数：932  字数：1342000  
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内容概要

本书在智能Agent的概念框架下，把人工智能中相互分离的领域统一起来。全书主体内容共分为六大部分，即问题解、知识与推理、合乎逻辑的行动、不确定知识与推理、学习，以及通信、感知与行动。本书通过Agent从感知外部环境、到实施行动、并最后对外部环境施加影响的全过程，将这六部分组织起来，形成一个相互联系的整体，使读者对人工智能有一个完整的概念，达到较好的效果。　　本书可以作为信息领域及相关领域的高等院校本科生和研究生的教科书或教学参考书，也可以作为相关领域的科研与工程技术人员的参考书。

作者简介

作者：(美国)拉塞尔 (tuartRussell) (美国)诺文 (PeterNorvig)

书籍目录

Ⅰ Artificial Intelligence　11　Introduction　31.1　What Is AI?　4·Acting humanly: The Turing Test approach　5·Thinking humanly: The cognitive modelling approach　6·Thinking rationally: The laws of thought approach　6·Acting rationally: The rational agent approach　71.2　The Foundations of Artificial Intelligence　8·Philosophy(428 B.C.-present)　8·Mathematics(c.800-present)　11·Psychology (1879-present)　12·Computer engineering(1940-present)　14·Linguistics(1957-present)　151.3　The History of Artificial Intelligence　16·The gestation of artificial Intelligence(1943-1956)　16·Early enthusiasm, great expectations(1952-1969)　17·A dose of reality(1966-1974)　20·Knowledge-based systems: The key to power?(1969-1979)　22·AI becomes an Industry(1980-1988)　24·The return of neural networks(1986-present)　24·Recent events(1987-present)　251.4　The State of the Art　261.5　Summary　27Bibliographical and Historical Notes　28Exercises　282　Intelligent Agents　312.1　Introduction　312.2　How Agents Should Act　31·The Ideal mapping from percept sequences to actions　34·Autonomy　352.3　Structure of Intelligent Agents　35·Agent programs　37·Why not just look up the answers?　38·An example　39·Simple reflex agents　40·Agents that keep track of the world　41·Goal-based agents　42·Utility-based agents　442.4　Environments　45·Properties of environments　46·Environment programs　472.5　Summary　49·Bibliographical and Historical Notes　50·Exercises　50Ⅱ　Problem-solving　533　Solving Problems by Searching　553.1　Problem-Solving Agents　553.2　Formulating Problems　57·Knowledge and problem types　58·Well-defined problems and solutions　60·Measuring problem-solving performance　61·Choosing states and actions　613.3　Example Problems　63·Toy Problems　63·Real-world problems　683.4　Searching for Solutions　70·Generating action sequences　70·Data structures for search trees　723.5　Search Strategies　73·Breadth-first search　74·Uniform cost search　75·Depth-first search　77·Depth-limited search　78·Iterative deepening search　78·Bidirectional search　80·Comparing search strategies　813.6　Avoiding Repeated States　823.7　Constraint Satisfaction Search　833.8　Summary　85Bibliographical and Historical Notes　86Exercises　874　Informed Search Methods　924.1　Best-First Search　92·Minimize estimated cost to reach a goal:Greedy Search　93·Minimizing the total path cost: A* search　964.2　Heuristic Functions　101·The effect of heuristic accuracy on performance　102·Inventing heuristic functions　103·Heuristics for constraint satisfaction problems　1044.3　Memory bounded Search　106·Iterative deepening A* search (IDA*)　106·SMA* search　1074.4　Iterative Improvement Algorithms　111·Hill-climbing search　111·Simulated annealing　113·Applications in constraint Satisfaction problems　1144.5　Summary　115Bibliographical and Historical Notes　115Exercises　1185　Game Playing　1225.1　Introduction: games as Search Problems　1225.2　Perfect Decisions in Two-Person Games　1235.3　Imperfect Decisions　126·Evaluation functions　127·Cutting off search　1295.4　Alpha-Beta Pruning　129·Effectiveness of alpha-beta pruning　1315.5　Games That Include an Element of Chance　133·Position evaluation in games with chance nodes　135·Complexity of expectiminimax　1355.6　State-of-the-Art Game Programs　136·Chess　137·Checkers or Draughts　138·Othello　138·Backgammon　139·Go 1395.7　Discussion　1395.8　Summary　141Bibliographical and Historical Notes　141Exercises　145Ⅲ　Knowledge and reasoning　1496　Agents that Reason Logically　1516.1　A Knowledge-Based Agent　1516.2　The Wumpus World Environment　153·Specifying the environment　154·Acting and reasoning in the wumpus world　1556.3　Representation, Reasoning, and Logic　157·Representation　160·Inference　163·Logics　1656.4　Propositional Logic: A Very Simple Logic　166·Syntax　166·Semantics　168·Validity and inference　169·Models　170·Rules of inference for propositional logic　171·Complexity of propositional inference　1736.5　An Agent for the Wumpus World　174·The knowledge base　174·Finding the wumpus　175·Translating knowledge into action　176·Problems with the propositional agent　1766.6　Summary　178Bibliographical and Historical Notes　178Exercises　1807　First-Order Logic　1857.1　Syntax and Semantics　186·Terms　188·Atomic sentences　189·Complex sentences　189·Quantifiers　189·Equality　1937.2　Extensions and Notational Variations　194·Higher-order logic　195·Functional and predicate expressions using the λ operator　195·The uniqueness quantifier Э!　196·The uniqueness operator ι　196·Notational variations　1967.3　Using First-Order Logic　197·The kinship domain　197·Axioms, definitions, and theorems　198·The domain of sets　199·Special notations for sets, lists and arithmetic　200·Asking questions and getting answers　2007.4　Logical Agents for the Wumpus World　2017.5　A Simple Reflex Agent　202 Limitations of simple reflex agents　2037.6　Representing Change in the World　203·Situation calculus　204·Keeping track of location　2067.7　Deducing Hidden Properties of the World　2087.8　Preferences Among Actions　2107.9　Toward a Goal-Based Agent　2117.10　Summary　211Bibliographical and Historical Notes　212Exercises　2138　Building a knowledge Base　2178.1　Properties of Good and Bad Knowledge Bases　2188.2　Knowledge Engineering　2218.3　The Electronic Circuits Domain　223·Decide what to talk about　223·Decide on a vocabulary　224·Encode general rules　225·Encode the specific instance　225·Pose queries to the inference procedure　2268.4　General Ontology　226·Representing Categories　229·Measures　231·Composite objects　233·Representing change with events　234·Times, intervals, and actions　238·Objects revisited　240·Substances and objects　241·Mental events and mental objects　243·Knowledge and action　2478.5　The Grocery Shopping World　247·Complete description of the shopping simulation　248·Organizing knowledge　249·Menu-planning　249·Navigating　252·Gathering　253·Communicating　254·Paying　2568.6　Summary　256Bibliographical and Historical Notes　256Exercises　2619　Inference in First-Order Logic　2659.1　Inference Rules Involving Quantifiers　2659.2　An Example Proof　2669.3　Generalized Modus Ponens　269·Canonical form　270·Unification　270·Sample proof revisited　2719.4　Forward and Backward Chaining　272·Forward-chaining algorithm　273·Backward-chaining algorithm　2759.5　Completeness　2769.6　Resolution: A Complete Inference Procedure　277·The resolution inference rule　278·Canonical forms for resolution　278·Resolution Proofs　279·Conversion to Normal Form　281·Example proof　282·Dealing with equality　284·Resolution strategies　2849.7　Completeness of resolution　2869.8　Summary　290Bibliographical and Historical Notes　291Exercises　29410　Logical Reasoning Systens　29710.1　Introduction　29710.2　Indexing, Retrieval, and Unification　299·Implementing sentences and terms　299·Store and fetch　299·Table-based indexing　300·Tree-based indexing　301·The unification algorithm　30210.3　Logic Programming Systems　304·The Prolog language　304·Implementation　305·Compilation of logic programs　306·Other logic programming languages　308·Advanced control facilities　30810.4　Theorem Provers　310·Design of a theorem prover　310·Extending Prolog　311·Theorem provers as assistants　312·Practical uses of theorem provers　31310.5　Forward-Chaining Production Systems　313·Match phase　314·Conflict resolution phase　315·Practical uses of production systems　31610.6　Frame Systems and Semantic Networks　316·Syntax and semantics of semantic networks　317·Inheritance with exceptions　319·Multiple inheritance　320·Inheritance and change　320·Implementation of semantic networks　321·Expressiveness of semantic networks　32310.7　Description Logics　323·Practical uses of description logics　32510.8　Managing Retractions, Assumptions, and Explanations　32510.9　Summary　327Bibliographical and Historical Notes　328Exercises　332Ⅳ Acting logically　33511　Planning　33711.1　A Simple Planning Agent　33711.2　From Problem Solving to Planning　338011.3　Planning in Situation Calculus　34111.4　Basic Representations for Planning　343·Representations for states and goals　343·Representations for actions　344·Situation space and plan space　345·Representations for plans　346·Solutions　34911.5　A Partial-Order Planning Example　34911.6　A Partial-Order Planning Algorithm　35511.7　Planning with Partially Instantiated Operators　35711.8　Knowledge Engineering for Planning　359·The blocks world　359·Shakey's world　36011.9　Summary　362Bibliographical and Historical Notes　363Exercises　36412　Practical Planning　36712.1　Practical Planners　367·Spacecraft assembly, integration, and verification　367·Job shop scheduling　369·Scheduling for space missions　369·Buildings, aircraft carriers, and beer factories　37112.2　Hierarchical Decomposition　371·Extending the language　372·Modifying the planner　37412.3　Analysis of Hierarchical Decomposition　375·Decomposition and sharing　379·Decomposition versus approximation　38012.4　More Expressive Operator Descriptions　381·Conditional effects　381·Negated and disjunctive goals　382·Universal quantification　383·A planner for expressive operator descriptions　38412.5　Resource Constraints　386·Using measures in planning　386·Temporal constraints　38812.6　Summary　388Bibliographical and Historical Notes　389Exercises　39013　Planning and Acting　39213.1　Conditional Planning　393·The nature of conditional plans　393·An algorithm for generating conditional plans　395·Extending the plan language　39813.2　A Simple Replanning Agent　401·Simple replanning with execution monitoring　40213.3　Fully Integrated Planning and Execution　40313.4　Discussion and Extensions　407·Comparing conditional planning and replanning　407·Coercion and abstraction　40913.5　Summary　410Bibliographical and Historical Notes 411Exercises　412Ⅴ　Uncertain knowledge and reasoning　41314　Uncertainty　41514.1　Acting under Uncertainty　415·Handing uncertain knowledge　416·Uncertainty and rational decisions　418·Design for a decision-theoretic agent　41914.2　Basic Probability Notation　420·Prior probability　420·Conditional probability　42114.3　The Axioms of Probability　422·Why the axioms of probability are reasonable　423·The joint probability distribution　42514.4　Bayes' Rule and Its Use　426·Applying Bayes' rule: The simple case　426·Normalization　427·Using Bayes' rule: Combining evidence　42814.5　Where Do Probabilities Come From?　43014.6　Summary　431Bibliographical and Historical Notes　431Exercises　43315　Probabilistic Reasoning Systems　43615.1　Representing Knowledge in an Uncertain Domain　43615.2　The Semantics of Belief Networks　438·Representing the joint probability distribution　439·Conditional independence relations in belief networks　44415.3　Inference in Belief Networks　445·The nature of probabilistic inferences　446·An algorithm for answering queries　44715.4　Inference in Multiply Connected Belief Networks　453·Clustering methods　453·Cutset conditioning methods　454·Stochastic simulation methods　45515.5　Knowledge Engineering for Uncertain Reasoning 456·Case study: The Pathfinder system　45715.6　Other Approaches to Uncertain Reasoning 458·Defaulet reasoning　459·Rule-based methods for uncertain reasoning　460·Representing ignorance: Dempster-Shafer theory　462·Representing vagueness: Fuzzy sets and fuzzy logic　46315.7　Summary　464Bibliographical and Historical Notes　464Exercises　46716　Making Simple Decisions　47116.1　Combining Beliefs and Desires Under Uncertainty　47116.2　The Basis of Utility Theory　473·Constraints on rational preferences　473·…and then there was Utility　47416.3　Utility Functions　475·The utility of money　476·Utility　Scales and utility assessment　47816.4　Multiattribute utility functions　480·Dominance　481·Preference structure and multiattribute utility　48316.5　Decision Networks　484·Representing a decision problem using decision net works　484·Evaluating decision networks　48616.6　The Value of Information　487·A simple example　487·A general formula　488·Properties　of the value of information　489·Implementing an information-gathering agent　49016.7　Decision-Theoretic Expert Systems　49116.8　Summary　493Bibliographical and Historical Notes　493Exercises　49517　Making Complex Decision Problems　49817.1　Sequential Decision Problems　49817.2　Value Iteration　50217.3　Policy Iteration　50517.4　Decision-Theoretic Agent Design　508·The decision cycle of a rational agent　508·Sensing in uncertain worlds　51017.5　Dynamic Belief Networks　51417.6　Dynamic Decision Networks　516·Discussion　51817.7　Summary　519Bibliographical and Historical Notes　520Exercises　521Ⅵ　Learning　52318　Learning from Observations　52518.1　A General Model of Learning Agents　525·Components of the performance element　527·Representation of the components　528·Available feedback　528·Prior knowledge　528·Bringing it all together　52918.2　Inductive Learning　52918.3　Learning Decision Trees　531·Decision trees as performance elements　531·Expressiveness of decision trees　532·Inducing decision trees from examples　534·Assessing the performance of the learning algorithm　538·Practical uses of decision tree learning　53818.4　Using Information Theory　540·Noise and overfitting　542·Broadening the applicability of decision trees　54318.5　Learning General Logical Descriptions　544·Hypotheses　544·Examples　545·Current-best-hypothesis search　546·Least-commitment search　549·Discussion 55218.6　Why Learning Works: Computational Learning Theory　552·How many examples are needed?　553·Learning decision lists　555·Discussion 55718.7　Summary　558Bibliographical and Historical Notes　559Exercises　56019　Learning in Neural and Belief Networks　56319.1　How the Brain Works　564·Comparing brains with digital computers　56519.2　Neural Networks　567·Notation　567·Simple computing elements　567·Network structures　570·Optimal network structure　57219.3　Perceptrons　573·What perceptrons can represent　573·Learning linearly separable functions　57519.4　Multilayer Feed-Forward Networks　578·Back-propagation learning　578·Back-propagation as gradient descent search　580·Discussion　58319.5　Applications of Neural Networks　584·Pronunciation　585·Handwritten character recognition　586·Driving　58619.6　Bayesian Methods for Learning Belief Networks　588·Bayesian learning　588·Belief network learning problems　589·Learning networks with fixed structure　589·A comparison of belief networks and neural networks　59219.7　Summary　593Bibliographical and Historical Notes　594Exercises　59620　Reinforcement Learning　59820.1　Introduction　59820.2　Passive Learning in a Known Environment　600·Naive updating 601·Adaptive dynamic programming　603·Temporal difference learning　60420.3　Passive Learning in an Unknown Environment　60520.4　Active Learning in an　Unknown Environment　60720.5　Exploration　60920.6　Learning an Action-Value Function　61220.7　Generalization in Reinforcement Learning　615·Applications to game-playing　617·Application to robot control　61720.8　Genetic Algorithms and Evolutionary Programming　61920.9　Summary　621Bibliographical and Historical Notes　622Exercises　62321　Knowledge in Learning　62521.1　Knowledge in Learning　625·Some simple examples　626·Some general schemes　62721.2　Explanation-Based Learning　629·Extracting general rules from examples　630·Improving efficiency　63121.3　Learning Using Relevance Information　633·Determining the hypothesis space　633·Learning and using relevance information　63421.4　Inductive Logic Programming　636·An example　637·Inverse resolution　639·Top-down learning methods　64121.5　Summary　644Bibliographical and Historical Notes　645Exercises　647Ⅶ　Communicating, perceiving, and acting　64922　Agents that Communicate　65122.1　Communication as Action　652·Fundamentals of language　654·The component steps of communication　655·Two models　of communication　65922.2　Types of Communicating Agents　659·Communicating using Tell and Ask　660·Communicating using formal language　661·An agent that communicates　66222.3　A Formal Grammar for a Subset of English　662·The Lexicon of ε0　664·The Grammar ofε0　66422.4　Syntactic Analysis(Parsing)　66422.5　Definite Clause Grammar(DCG)　66722.6　Augmenting a Grammar　668·Verb Subcategorization　669·Generative Capacity of Augmented Grammars　67122.7　Semantic Interpretation　672·Semantics as DCG Augmentations　673·The semantics of “john loves Mary”　673·The semantics ofε1·Converting quasi-logical form to logical form　677·Pragmatic Interpretation　67822.8　ambiguity and Disambiguation　680·Disambiguation　68222.9　A Communicating Agent　68322.10　Summary　684Bibliographical and Historical Notes　685Exercises　68823　Practical Natural Language Processing　69123.1　Practical Applications　691·Machine translation　691·Database access　693·Information retrieval　694·Text categorization　695·Extracting data from text　69623.2　Efficient Parsing　701·Extracting parses from the chart: Packing　70123.3　Scaling Up the Lexicon　70323.4　Scaling Up the Grammar　705·Nominal compounds and apposition　706·Adjective phrases　707·Determiners　708·Noun phrases revisited　709·Clausal complements　710·Relative clauses　710·Questions　711·Handling agrammatical strings　71223.5　Ambiguity　712·Syntactic evidence　713·Lexical evidence　713·Semantic evidence　713·Metonymy　714·Metaphor　71523.6　Discourse Understanding　715·The structure of coherent discourse　71723.7　Summary　719Bibliographical and Historical Notes　720Exercises　72124　Perception　72424.1　Introduction　72424.2　Image Formation　725·Pinhole camera　725·Lens systems　727·Photometry of image formation　729·Spectrophotometry of image formation　73024.3　Image-Processing Operations for Early Vision　730·Convolution with linear filters　732·Edge detection　73324.4　Extracting3-D Information Using Vision　734·Motion　735·Binocular stereopsis　737·Texture gradients　742·Shading　743·Contour　74524.5　Using Vision for Manipulation and Navigation　74924.6　Object representation and Recognition　751·The alignment method　752·Using projective invariants　75424.7　Speech Recognition　757·Signal processing　758·Defining the overall speech recognition model　760·The language model: P(words)　760·The acoustic model: P(signallwords)　762·Putting the models together　764·The search algorithm　765·Training the model　76624.8　Summary　767Bibliographical and Historical Notes　767Exercises　771 25　Robotics　77325.1　Introduction　77325.2　Tasks: What Are Robots Good For?　774·Manufacturing and materials handling　774·Gofer robots　775·Hazardous environments　775·Telepresence and virtual reality　776·Augmentation of human abilities　77625.3　Parts: What are Robots Made Of?　777·Effectors: Tools for action　777·Sensors: Tools for Perception　78225.4　Architectures　786·Classical architecture　787·Situated automata　78825.5　Configuration Spaces: A Framework for Analysis　790·Generalized configuration space　792·Recognizable Sets　79525.6　Navigation and Motion Planning　796·Cell decomposition　796·Skeletonization methods　798·Fine-motion planning　802·Landmark-based navigation　805·Online algorithms　80625.7　Summary　809Bibliographical and Historical Notes　809Exercises　811Ⅷ　Conclusions　81526　Philosophical Foundations　81726.1　The Big Questions　81726.2　Foundations of Reasoning and Perception　81926.3　On the Possibility of Achieving Intelligent Behavior　822·The mathematical objection　824·The argument from informality　82626.4　Intentionality and Consciousness　830·The Chinese Room　831·The Brain Prosthesis Experiment　835·Discussion　83626.5　Summary　837Bibliographical and Historical Notes　838Exercises　84027　AI: Present and Future　84227.1　Have We Succeeded Yet?　84227.2　What Exactly Are We Trying to Do?　84527.3　What If we Do Succeed?　848A　Complexity analysis and O() notation　851A.1　Asymptotic Analysis　851A.2　Inherently Hard Problems　852Bibliographical and Historical Notes　853B　Notes on Languages and Algorithms　854B.1　Defining Languages with Backus-Naur Form(BNF)　854B.2　Describing Algorithms with Pseudo-Code　855·Nondeterminism　855·Static variables　856·Functions as values　856B.3　The Code Repository　857B.4　Comments　857Bibliography　859Index　905

用户评论 (总计7条)

•   不是我崇洋媚外，看了老外的书经常能让人豁然开朗，眼前一亮，常常折服于他们看待事物的深度和方法，说的简单是白痴都看得懂，前提是你有英语基础，毕竟这是英文版的...
•   要花时间，但这本书确实全面，我在图书管找到的都没它全
•   以前也看过一部分，现在上课时正在用，想好好读一下，真的很不错的一本书
•   难度不是很大，要求一定的英语水平。
•   讲解得很明白，适合入门，但因为关注面广，所以相应的深度打了折扣，但仍不失为经典，推荐
•   不是第二版的不满意！
•   书倒是好书，但是纸质太差，拿在手里感觉不舒服，还不如看电子版的