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理论与实践(英文版)
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人工智能——理论与实践(英文版)计算机_人工智能_综合教材_研究生/本科/专科教材_工学_计算机教材_计算机教材_本科/研究生_计算机专业教材_计算机专业课程_人
 

内容简介

   这是一本阐述人工智能基本理论及其实际应用的教材,由三位资深的人工智能专家精心编著而成。针对机器智能系统开发中涌现出的表达与计算问题,本书介绍了最新的研究成果,并讨论了系统实现中涉及到的实际问题。作者深入探讨了用于解决学习、规划和不确定性问题的传统符号推理技术,例如演绎推理、对策树等,并介绍了神经网络、概率推理等新技术。书中出现的重要算法在每章后面都附有其Lisp实现的源代码,以供读者在实验时进行参考。另外,本书还给出了丰富的人工智能应用系统的实例。
   本书可作为高等院校计算机、控制、机电、数学等专业人工智能课程的教材,也可供从事人工智能研究及应用的科学工作者和工程技术人员学习参考。
  

目录

introduction
robot explorers, 2
1.1 artificial intelligence in practice 3
examples of artificial intelligence systems, 4
1.2 artificial intelligence theory 5
examples of artificial intelligence theory, 6
1.3 identifying and measuring intelligence
1.4 computational theories of behavior 9
representation, 10
syntax and semantics, 11
1.5 automated reasoning 12
inference and symbolic manipulation, 13
representing common-sense knowledge, 14
combinatorial problems and search, 14
complexity and expressivity, 15
1.6 how this book is organized 16
summary 18
background 19
exercises 20
symbolic programming

.2.1 rule-based reactive system example 25
representing sensors and sensor values as symbols, 26
2.2 introduction to lisp 27
language requirements,27
common lisp, 27
lists and lisp syntax, 28
symbols, 28
programs and documentation, 28
2.3 interacting with lisp 29
the lisp interpreter, 29
2.4 functions in lisp 31
function invocation, 31
procedural abstraction, 32
conditional statements, 33
recursive functions, 35
evaluating functions in fa. les, 35
2.5 environments, symbols, and scope 36
assigning values to symbols, 36
eval and apply revisited, 37
structured environments, 38
variables, 39
lexical scoping, 40
2.6 more on functions 42
functions with local state, 42
lambda and functions as arguments, 43
2.7 list processing 44
suspending evaluation using quote, 44
building and accessing elements in lists, 45
lists in memory, 45
modifying list structures in memory, 46
alternative parameter-passing conventions, 47
predicates on lists, 48
built-in list manipulation functions, 48
optional arguments, 49
list-processing examples, 49
data abstraction, 51
2.8 iterative constructs 53
mapping functions to arguments, 53
general iteration, 54
simple iteration, 55
2.9 monitoring and debugging programs 56
tracing and stepping through programs, 56
formatted output, 58
2.10 rule-based reactive system revisited 58
summary 64
background 65
exercises 65
representation and logic
3.1 propositional logic 73
syntax for p, 74
semantics for p, 75
32 formal system for/v 76
logical axioms of p, 77
normal forms, 78
rules of inference, 79
proofs and theorems, 79
resolution rule of inference, 80
completeness, soundness, and decidability, 81
computational complexity, 82
solving problems with logic, 82
3.3 automated theorem proving in p 84
goal reduction in p, 85
proof by contradiction, 87
3.4 predicate calculus 88
syntax for pc, 89
translating english sentences into logic, 90
more about quantification, 91
semantics for pc, 91
3.5 formal system for pc 93
specifying programs in prolog, 94
eliminating quantifiars, 94
learning and deductive inference, 96
decidability, 98
3.6 automated theorem proving in pc 99
matching and universal instantiation, 99
goal reduction in pc, 101
unification, 103
concept description languages, 107
semantic networks, 108
3.7 nonmonotonic logic 109
closed-world assumption, 109
abductive and default reasoning, 111
minimal models, 112
3.8 deductive retrieval systems 113
forward and backward chaining, 114
reason maintenance systems, 116
nonmonotonic data dependencies, 118
summary 119
background 121
exercises 122
lisp implementation: data dependencies 127
search
4.1 basic search issues 133
search spaces and operators, 134
appliance assembly example, 135
exploiting structure to expedite search, 136
4.2 blind search 137
depth-first search, 138
depth-first search is space efficient, 139
breadth-first search, 140
breadth-first search is guaranteed, 141
iterative-deepening search, 141
iterative-deepening search is asymptotically optimal, 143
searching in graphs, 144
4.3 heuristic search 144
best-first search, 145
admissible evaluation functions, 146
4.4 optimization and search 149
hill-climbing search, 149
local minima and maxima, 151
gradient search, 153
simulated annealing, 153
simulated evolution and genetic algorithms, 154
application to vehicle routing, 158
4.5 adversary search 160
minimax search, 160
a-b search, 163
4.6 indexing in discrimination trees 166
storing and retrieving predicate calculus formulas, 167
decision trees, 168
summary 169
background 171
exercises 171
lisp implementation: discrimination trees 174
5 learning
5.1 classifying inductive learning problems 180
supervised learning, 180
classification and concept learning, 182
unsupervised learning, 183
online and batch learning methods, 183
52 theory of inductive inference 183
the role of inductive bias, 184
restricted hypothesis space biases, 184
preference biases, 185
probably approximately correct learning, 186
pac learnable concept classes, 187
finding consistent hypotheses, 188
5.3 version spaces 188
attributes, features, and dimensions, 189
specializing and generalizing concepts, 190
maintaining version-space boundaries, 191
data structures for learning, 192
implementing the version-space method, 194
optimal method for conjunctions of positive literals, 195
5.4 decision trees 195
implementing a preference for small decision trees, 196
disorder and information theory, 199
decision trees in practice, 202
5.5 network learning methods 202
model for computation in biological systems, 2113
adjustable weights and restricted hypothesis spaces, 205
5.6 gradient guided search 206
searching in linear function spaces, 207
experimental validation, 208
nonlinear function spaces and artificial neural
networks, 210
deriving the gradient for multilayer networks, 211
error backpropagation procedure, 212
implementing artificial neural networks in lisp, 214
representational and computational issues, 217
networks with adjustable thresholds, 218
comparing the performance of different networks, 220
5.7 perceptrons 221
perceptron learning rule, 222
linearly separable funct/ons, 223
5.8 radial basis functions 224
approximating functions by combining gaussians, 225
two-step strategy for adjusting weights, 227
functions with multidimensional input spaces, 230
5.9 learning in dynamic environments 231
reinforcement learning. 231
computing an optimal policy, 235
online methods for learning value functions, 235
learning by exploration. 239
summary 24o
background 242
exercises 243
lisp implementation: learning algorithms 249
6 advanced representation
6.1 temporal reasoning 256
6.2 the situation calculus 257
constraining fluents in situations, 260
frame problem, 260
qualification problem, 262
6.3 first-order interval temporal logic 264
syntax for the interval logic, 265
representing change in the interval logic, 267
semantics for the interval logic, 268
6.4 managing temporal knowledge 269
6.5 knowledge and belief 273
possible-worlds semantics, 277
6.6 spatial reasoning 279
representing spatial knowledge, 279
planning paths in configuration space, 281
path planning as graph search, 282
locally distinctive places, 285
summary 286
background 287
exercises 288
lisp implementation: temporal reasoning 291
planning
7.1 state-space search 298
what is planning?, 298
planning as .%arch, 300
representing and solving search problems, 301
state progression, 3o2
goal regression, 303
means/ends analysis,
machine assembly example, 305
operant schemas, 306
block-stacking problems, 307
7.2 least commitment planning 308
search in the space of partially ordered plans, 309
sound, complete, andsystematic search, 312
block-stacking example, 313
recognizing and resolving conflicts, 316
variables in par'dally ordered plans, 317
7.3 planning in a hierarchy of abstraction spaces 320
analysis of planning with levels of abstraction, 321
towers-of-hanoi problems, 322
task reduction planning, 325
7.4 adapting previously generated plans 326
indexlng, retrieving, and adapting plans, 326
analysis of adaptive planning, 331
7.5 planning with incomplete information 332
the copier-repair problem, 332
generating conditional plans, 335
contexts represent possible sets of observations, 336
7.6 more expressive models of action 340
conditional effects, 341
isjunctive preconditions, 342
universally quantified effects, 343
wandering briefcase example, 344
processes outside the planner's control, 345
summary 346
background 347
exercises 348
lisp implementation: refining partially ordered
plans 351
uncertainty
8.1 motivation for reasoning under uncertainty 357
sources of uncertainty, 357
representing uncertain knowledge, 357
applications involving uncertainty, 358
8.2 probability theory 359
frequency interpretation of probability, 359
save interpretation of probability, 359
desrees of belief, 360
random variables and distributions, 361
conditional probability, 362
calculus for combining probabilities,
conclitional independence, 366
maintaining consistency, 367
8.3 probabilisfic networks 368
graphical models, 369
path-based characterization of independence, 371
quantifying probabilistic networks, 372
inference in probabflistic networks, 373
exact inference in tree-structured networks, 374
propagating evidence in trees, 378
exact inference in singly connected networks, 380
approximate inference using stochastic simulation, 382
likelihood-weighting algorithm, 384
probabilistic reasoning in medicine, 386
8.4 decision theory 388
preferences and utilities, 389
decision tree methods, 390
computing the value of information, 393
automated decision making in medidne, 394
summary 395
background 396
exercises 397
lisp implementation: inference in probabilistic
networks 399
9 image understanding
9.1 sensors and images 410
digital images, 410
noise in image processing, 410
9.2 computer vision 412
understanding images, 413
vision versus thought, 414
9.3 human vision 415
transferring information from the eye to the brain, 415
compressing visit information, 417
9.4 vision as a recovery problem 418
what to recover, 420
geometric aspects of image formation, 420
perspective projection, 420
orthographic projection, 423
paraperspective projection, 425
shape representation, 426
surface orientation and shape under perspective, 426
surface orientation and shape under orthography, 426
stereographic projection, 427
geometric properties of the perspective projection, 427
imaging with tenses, 430
photometric aspects of image formation, 430
9.5 recovery of image descriptions 431
edge detection, 431
differentiation approaches, 432
model-based approaches, 436
edge grouping and hough transform, 437
image segmentation, 438
9.6 shape from contour 440
qualitative analysis using edge labels, 441
quantitative analysis using skewed symmetries, 442
9.7 shape from shading 444
reflectance maps, 445
solving ill-posed problems, 448
photometric stereo, 449
9.8 shape from texture 450
density of textural elements, 450
textural reflectance maps, 451
9.9 stereo 453
addressing the correspondence problem, 453
intensity-based matching, 455
edge-based matching, 456
9.10 analysis of visual motion 457
motion fields, 458
motion field estimation, 460
motion field interpretation, 463
9.11 active vision 465
9.12 applications 466
autonomous vehicle navigation, 467
object recognition, 469
summary 471
background 474
exercises 476
lisp implementation: labeling polyhedral scenes
10 natural language processing
10.1 components of language 491
ccmtent and function words, 491
structure of phrases, 492
10.2 context-free grammars 493
parsing, 495
10.3 parsing context-free grammars 496
exploiting the lexicon, 498
building a parse tree, 499
10.4 grammars involving features 502
matching with features, 5o5
10.5 efficient parsing with charts 507
ambiguous sentences, 507
10.6 semantic interpretation 511
word senses, 512
semantic interpretation using features, 515
disambiguating word senses, 517
10.7 generating natural language 519
10.8 natural language in context 521
speech acts, 521
establishing reference, 522
handling database assertions and queries, 524
10.9 quantifier scoping 529
summary 530
background 530
exercises 531
lisp implementation: simple parser 533
bibliography
vocabulary index
code index

前言

   his book is designed to introduce students to a set of theoretical and computational techniques that serve as a foundation for the study of artificial intelligence (Al). The presentation is aimed at students with a background in computer science at about the sophomore or junior level in college. The emphasis is on algorithms and theoretical machinery for building and analyzing AI systems. Traditional symbolic AI techniques such as deductive inference, game-tree search, and natural language parsing are covered, as are hybrid approaches such as those employed in neural networks, probabilistic inference, and machine vision. The coverage is broad, with selected topics explored in greater depth but with no attempt to exhaustively survey the entire field.
   Representation
   The book focuses on the importance of representation in the core chapters dealing with logic, search, and learning. It incorporates a more formal treatment of AI than is found in most introductory textbooks. This formal treatment is reflected in the attention given to syntax and semantics in logic and in the material concerning the computational complexity of AI algorithms.
   The material on learning draws on recent unifying work in computational learning theory to explain a variety of techniques from decision trees to neural networks.
   The book provides a consistent pedagogic example of AI in the real world through examples focusing on AI systems corresponding to robots and software automation "soffbots." A wide range of other examples are also introduced to characterize both the potential and the variety of Al applications. The chapters on natural language processing, planning, uncertainty, and vision supply a state-of-the-art perspective unifying existing approaches and summarizing challenging areas for future research.
   This book is not meant as an exhaustive survey of AI techniques. Subjects such as qualitative reasoning about physical systems and analogical reasoning are only briefly touched on in this text. Other subjects are given much more attention in this book than in traditional texts. Learning, planning, and probabilistic reasoning are treated in some depth, reflecting their increased importance in the field. The chapter on vision (Chapter 9, Image Understanding) is substantial in its coverage of topics to reflect the importance of perception in understanding intelligence and building artifacts that interact with the world in useful and interesting ways.
   Theory and Practice Although the text emphasizes theoretical foundations, practical problems involved with the implementation of AI algorithms are addressed in every chapter. A self-contained introduction to symbolic programming in Common Lisp is provided to encourage students to perform computational experiments. Lisp code is given for many of the important algorithms descried in the text; however, the text is designed so that the student can
  ignore Lisp and implementation issues altogether ff he or she chooses. The code uses a carefully chosen subset of Common Lisp to teach algorithmic issues in AI. In contrast, other texts use AI algorithms to teach Lisp programming techniques.
   All the algorithms in the text are described in English prose and pseudo code. In the case of algorithms that are also given in Lisp, most of the time the code appears in a Lisp Implementation appendix at the end of the chapter, but on some occasions it appears in the main body of the chapter. Code appears in the main body when it is considered particularly
  important that the student explore the underlying issues empirically. With most of the Lisp code relegated to appendices, instructors are free to choose the areas they want to emphasize empirically. The tight coupling between the descriptions of algorithms in the text and the accompanying Lisp code makes it easy for students to experiment, without the bother of using two
  texts with different perspectives and algorithmic approaches.
   We use Lisp instead of Prolog because Lisp is closest m structure to languages such as Pascal and C that students me likely to be familiar with. We use Lisp instead of Pascal or C because the list processing and symbolic manipulation routines available in Lisp aUow for elegant implementations of important algorithms that can be compacdy listed. Note, however, that a library of C++ code is available (see the section on "Supplements") that mirrors the Common Lisp treatment in the text function for function and algorithm for algorithm.
  To the Student Preface material is usually aimed at instructors who are thinking of adopting a text for a course. Generally, students cut straight to the first chapter or the table of contents to get some idea of what the book is about. Tkis book ts designed to teach students about the theory and practice of building computer programs that perform interesting and useful tasks. With the exception of some diversions in the introductory chapter, we leave the philosophical conundrums to the philosophers and focus on techniques, algorithms, and analytical tools that we believe students will find useful in building sophisticated (even intelligent) computer programs.
   The book describes precise problems, analyzes them from a computational perspective, and specifies efficient algorithms for their solution where possible. Along the way, we provide the necessary logic, computer science, and mathematics for you to understand the important issues and ultimately develop your own solutions and propose your own problems. Our hope is that you will find the techniques and ideas in this book useful, whether you pursue a career in engineering, computer science, business management, or any other area that requires you to think in terms of computational proceases that have to interact with a complex and changing world.
   To the Instructor The core material in the book is in the first five chapters covering basic introductory and motivational material, symbolic programming for courses interested in implementation, representation and logic, search, and learning. Within these core chapters, instructors have considerable flexibility regarding what to include and how much time to spend on particular topics.
   The choice of topics and allocation of lecture time will depend on the background of the students taking the course. Often students have a reasonable background in boolean logic from a previous course in computer science, engineering, or mathematics, in which case the chapter on representation and logic can move along rather quickly. Search issues are generally
  familiar to computer science students, and the basic blind-search methods, including depth-first and breadth-first search, should take very little time.
   Figure 1 This graph illustrates some of the dependencies and connections among the chapters in this text. A solid line indicates a strong dependency between two chapters, and a dashed line indicates a connection or conditional dependency between two chapters that an instructor may wish to consider.
   We recommend spending a significant amount of time on learning since the area is reasonably mature, the issues dramatically illustrate the role of representation and search, and students are generally fascinated with the prospect of building systems that learn.
   Representation appears before search in the order of chapters because representation is the more fundamental idea as far as Al is concerned. We emphasize logic because it enables students to think precisely about representation. Pedagogically, there are situations that merit covering search before representation; if you are teaching Lisp and this is the students' first exposure to Lisp and symbolic programming, consider covering the search chapter first because the examples of search procedures provide a somewhat easier introduction to Lisp programming issues. With the exception of the section on 'discrimination networks at the end of the search chapter, the representation and search chapters can be covered in either order.

.   Figure 1 illustrates some of the dependencies and connections among the chapters in this text. A sohd line indicates that one chapter should be covered before another chapter in order to fully understand all the material.
   A dashed line indicates a connection between two chapters that an instructor may wish to emphasize or a conditional dependency that an instructor may wish to account for. For example, the section on spatial representation and robot navigation in Chapter 6 (Advanced Representation) can be used to motivate and set the stage for topics covered in Chapter 9 (Image Understanding). All the chapters are conditionally dependent on Chapter 2 (Symbolic Programming) if implementation issues are to be covered and the students require instruction in symbolic programming methods. Additional information regarding chapter dependencies and synergies as well as suggestions for course syllabi are available ill the Instructors Resource Guide (see the section on "Supplements").
   Supplements
   Source supplemental materials for this book are available via anonymous FTP from aw.c0m in the subdirectory aw/dean. The supplemental materials for the book include the following items:
   Instructor's Guide and Solutions Manual---contain notes on each chapter,
   solutions to selected exercises, additional exercises for the Lisp (Chapter 2) and vision (Chapter 9) chapters, and sample exams with answers. This guide is available only on a disk from the publisher (ISBN 32548-4).
   Selected figures---selected figures in encapsulated PostScript format are available for overhead transparencies.
   Source code the sample source code contained in the text is available in both Lisp and C++ implementations. Other implementations may be available (for example, the Scheme dialect of Lisp); check the README file in bc/dean for current status and recent developments.
   To obtain the supplemental materials, FTP to bc.aw.com as follows:%ftp av.com
  and log on as anonymous. Use your electronic mail address as your password and connect to the directory for this book by typing % cd av/dean Before retrieving supplements, it is a good idea to look at the REAl)ME file to see if changes have been made since this book went to press. You can retrieve this file by typing % get README Type quit to exit FIT' and read the README file. (Although you could read the file online, it is courteous not to load the FTP server while you are just reading.) Then log back on when you are ready to download the files that you want. Using FTP to retrieve archived files can get complicated. The REAl)ME file will give you some additional advice, but you may find it helpful to consult your favorite UNIX guide or local artwork wizard.
   Thanks
   We benefited from the efforts of many previous authors in organizing the material covered in this text and figuring out how to present the material to students. Irt particular, we would like to acknowledge the texts by Chamiak and McDermott [1985], Nilsson [1980], and Winston [1979] that
  provided our first introductions to AI. We are also indebted to Davis [1990], Genesereth and N'dsson [1987], Ginsberg [1993], and Winston [1992], whose books we consulted while writing this text.
   A lot of friends and colleagues contributed to this book by providing feedback to the authors during crucial times during its writing. In particular, we would like to thank the following people:
   Mark Boddy Robert Goldman Bart Selman
   Chris Brown Steve Hanks Jude Shavlik
   Jack Breese Eric Horvitz Yoav Shoham
   Eugene Chamiak Leslie Kaelbling Mike Shwe
   Ernie Davis Paris Kanellakis Austin Tate
   Mark Drurmnond Richard .Korf Prasad Tadepalli
   Charles Dyer Tom Mitchell Dan Weld
   Nort Fowler Ann Nicholson Mike Wellman
   The production and editorial help from Addison-Wesley Publishing Company was exceptional. We would like to thank Carter Shanklin, the Acquisitions Editor for Computer Science at Addison-Wesley, for shepherding us through the entire process. Judith Hibbard, the Senior Production Editor, Melissa Standen, the Editorial Assistant, and Ari Davidow, the Technical Production Assistant, were amazingly helpful at all the right times. We would also like to thank Sara Larson from the University of Maryland at College Park, who helped with the vision chapter, and Mary Andrade and Dawn Nichols from Brown University, who provided invaluable assistance in keeping track of the many drafts and handling the extensive correspondence that was required.
   A large number of students also provided feedback while we were writing the text, far too many to list, but the following deserve special mention:
   Scott Baetz Michael Littman
   Ted Camus Mike Perkowitz
   Lloyd Greenwald Kostadis Roussos
   Shieu-Hong Lin Smith Surasmith
   A special thanks goes to Jon Monsarrat for his unflagging enthusiasm for the project and for the many hours he spent hacking UNIX, PostScript, and LATEX. Finally, each of us would like to thank our respective spouses and loved ones for being supportive when it was needed most.
   Thomas Dean
   James Allen
   Yiannis Aloimonos
  

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