Artificial intelligence a new synthesis
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作 者：(美)[N.J.尼尔森]Nils J.Nilsson著
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ISBN：9787111074380
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简介
《人工智能英文版》介绍了人工智能领域中最重要的一个概念——智能代理。《人工智能英文版》从最基本的反应式代理入手，逐步向人们展示了现代人工智能不断增强的认知能力，同时也例证了该领域中重要且经久不衰的思维、思想。神经网络、遗传程序设计、计算机视觉、探视搜索、知识表示和推理、贝叶斯网络、规划和语言理解等有关人工智能的重要内容都通过《人工智能英文版》所描述的各种代理的不断增长的能力得以展现。《人工智能英文版》作者是人工智能领域的主要开创者和重要带头人，正是他提供给了广大读者一个耳目一新和富有生机的合成技术，该技术将领导人类把整个人工智能领域的研究引向一个新的境界。 Nils J. Nilsson: Artificial Intelligence, A New Synthesis. Copyright @ 1998 by Morgan Kaufmann Publishers, Inc. Harcourt Asia Pte Ltd under special arrangement with Morgan Kaufmann authorizes China Machine Press to print and exclusively distribute this edition, which is the only authorized complete and unabridged reproduction of the latest American Edition published and priced for sale in China only, not including Hong Kong SAR and Taiwan. Unauthorized export of this edition is a violation of the Copyright Act. Violation of this Law is subjected to Civil and Criminal penalties.
目录
contents
preface
introduction
1.1 what is al?
1.2 approaches to artificial intelligence
1.3 brief history of al
1.4 plan of the book
1.5 additional readings and discussion
exercises
1 reactive machines
2 stimulus-response agents
2.1 perception and action
2.1.1 perception
2.1.2 action
2.1.3 boolean algebra
2.1.4 classes and forms of boolean functions
2.2 representing and implementing action functions
2.2.1 production systems
2.2.2 networks
2.2.3 the subsumption architecture
.2.3 additional readings and discussion
exercises
neural networks
3.1 introduction
3.2 training single tlus
3.2.1 tlu geometry
3.2.2 augmented vectors
3.2.3 gradient descent methods
3.2.4 the widrow-hoff procedure
3.2.5 the generalized delta procedure
3 . 2.6 the error-correction procedure
3.3 neural networks
3.3.1 motivation
3.3.2 notation
3.3.3 the backpropagation method
3.3.4 computing weight changes in the final layer
3.3.5 computing changes to the weights in intermediate layers
3.4 generalization, accuracy, and overfitting
3.5 additional readings and discussion
exercises
4 machine evolution
4.1 evolutionary computation
4.2 genetic programming
4.2.1 program representation in gp
4.2.2 the gp process
4.2.3 evolving a wall-following robot
4.3 additional readings and discussion
exercises
5 state machines
5.1 representing the environment by feature vectors
5.2 elman networks
5.3 iconic representations
5.4 blackboard systems
5.5 additional readings and discussion
exercises
6 robot vision
6.1 introduction
6.2 steering an automobile
6.3 two stages of robot vision
6.4 image processing
6.4.1 averaging
6.4.2 edge enhancement
6.4.3 combining edge enhancement with averaging
6.4.4 region finding
6.4.5 using image attributes other than intensity
6.5 scene analysis
6.5.1 interpreting lines and curves in the image
6.5.2 model-based vision
6.6 stereo vision and depth information
6.7 additional readings and discussion
exercises
search in state spaces
7 agents that plan
7.1 memory versus computation
7.2 state-space graphs
7.3 searching explicit state spaces
7.4 feature-based state spaces
7.5 graph notation
7.6 additional readings and discussion
exercises
8 uninformed search
8.1 formulating the state space
8.2 components of implicit state-space graphs
8.3 breadth-first search
8.4 depth-first or backtracking search
8.5 iterative deepening
8.6 additional readings and discussion
exercises
9 heuristic search
9.1 using evaluation functions
9.2 a general graph-searching algorithm
9.2.1 algorithm a*
9.2.2 admissibility of a*
9.2.3 the consistency (or monotone) condition
9.2.4 iterative-deepening a*
9.2.5 recursive best-first search
9.3 heuristic functions and search efficiency
9.4 additional readings and discussion
exercises
10 planning, acting, and learning
10.1 the sense/plan/act cycle
10.2 approximate search
10.2.1 island-driven search
10.2.2 hierarchical search
10.2.3 limited-horizon search
10.2.4 cycles
10.2.5 building reactive procedures
10.3 learning heuristic functions
10.3.1 explicit graphs
10.3.2 implicit graphs
10.4 rewards instead of goals
10.5 additional readings and discussion
exercises
11 alternative search formulations and
applications
11.1 assignment problems
11.2 constructive methods
11.3 heuristic repair
11.4 function optimization
exercises
12 adversarial search
12.1 two-agent games
12.2 the minimax procedure
12.3 the alpha-beta procedure
12.4 the search efficiency of the alpha-beta procedure
12.5 other important matters
12.6 games of chance
12.7 learning evaluation functions
12.8 additional readings and discussion
exercises
iii knowledge representation and
reasoning
13 the propositional calculus
13.1 using constraints on feature values
13.2 the language
13.3 rules of inference
13.4 definition of proof
13.5 semantics
13.5.1 interpretations
13.5.2 the propositional truth table
13.5.3 satisfiability and models
13.5.4 validity
13.5.5 equivalence
13.5.6 entailment
13.6 soundness and completeness
13.7 the psat problem
13.8 other important topics
13.8.1 language distinctions
13.8.2 metatheorems
13.8.3 associative laws
13.8.4 distributive laws
exercises
14 resolution in the propositional calculus
14.1 a new rule of inference: resolution
14.1.1 clauses as wffs
14.1.2 resolution on clauses
14.1.3 soundness of resolution
14.2 converting arbitrary wffs to conjunctions of clauses
14.3 resolution refutations
14.4 resolution refutation search strategies
14.4.1 ordering strategies
14.4.2 refinement strategies
14.5 horn clauses
exercises
15 the predicate calculus
15.1 motivation
15.2 the language and its syntax
15.3 semantics
15.3.1 worlds
15.3.2 interpretations
15.3.3 models and related notions
15.3.4 knowledge
15.4 quantification
15.5 semantics of quantifiers
15.5.1 universal quantifiers
15.5.2 existential quantifiers
15.5.3 useful equivalences
15.5.4 rules of inference
15.6 predicate calculus as a language for representing knowledge
15.6.1 conceptualizations
15.6.2 examples
15.7 additional readings and discussion
exercises
16 resolution in the predicate calculus
16.1 unification
16.2 predicate-calculus resolution
16.3 completeness and soundness
16.4 converting arbitrary wffs to clause form
16.5 using resolution to prove theorems
16. 6 answer extraction
16.7 the equality predicate
16.8 additional readings and discussion
exercises
17 knowledge based systerms
17.1 confronting the real world
17.2 reasoning using horn clauses
17.3 maintenance in dynamic knowledge bases
17.4 rule-based expert systems
17.5 rule learning
17.5.1 learning propositional calculus rules
17.5.2 learning first-order logic rules
17.5.3 explanation-based generalization
17.6 additional readings and discussion
exercises
18 representing commonsense knowledge
18.1 the commonsense world
18.1.1 what is commonsense knowledge?
18.1.2 difficulties in representing commonsense knowledge
18.1.3 the importance of commonsense knowledge
18.1.4 research areas
18.2 time
18.3knowledge representation by networks
18.3.1 taxonomic knowledge
18.3.2 semantic networks
18.3.3 nonmonotonic reasoning in semantic networks
18.3.4 frames
18.4 additional readings and discussion
exercises
19 reasoning with uncertain information
19.1 review of probability theory
19.1.1 fundamental ideas
19.1.2 conditional probabilities
19.2 probabilistic inference
19.2.1 a general method
19.2.2 conditional independence
19.3 bayes networks
19.4 patterns of inference in bayes networks
19.5 uncertain evidence
19.6 d-separation
19.7probabilistic inference in polytrees
19.7.1 evidence above
19.7.2 evidence below
19.7.3 evidence above and below
19.7.4 a numerical example
19.8 additional readings and discussion
exercises
20 learning and acting with bayes nets
20.1 learning bayes nets
20.1.1 known network structure
20.1.2 learning network structure
20.2 probabilistic inference and action
20.2.1 the general setting
20.2.2 an extended example
20.2.3 generalizing the example
20.3 additional readings and discussion
exercises
iv planning methods based on
logic
21 the situation calculus
21.1 reasoning about states and actions
21.2 some difficulties
21.2.1 frame axioms
21.2.2 qualifications
21.2.3 ramifications
21.3 generating plans
21.4 additional readings and discussion
exercises
22 planning
22.1 strips planning systems
22.1.1 describing states and goals
22.1.2 fomward search methods
22.1.3 recursive strips
22.1.4 plans with run-time conditionals
22.1.5 the sussman anomaly
22.1.6 backward search methods
22.2 plan spaces and partial-order planning
22.3 hierarchical planning
22.3.1 abstrips
22.3.2 combining hierarchical and partial-order 22 planning
22.4 learning plans
22.5 additional readings and discussion
exercises
v communication and integration
2.3 multiple agents
23.1 interacting agents
23.2 models of other agents
23.2.1 varieties of models
23.2.2 simulation strategies
23.2.3 simulated databases
23.2.4 the intentional stance
23.3 a modal logic of knowledge
23.3.1 modal operators
23.3.2 knowledge axioms
23.3.3 reasoning about other agents’ knowledge
23.3.4 predicting actions of other agents
23.4 additional readings and discussion
exercises
24 communication among agents
24.1 speech acts
24.1.1 planning speech acts
24.1.2 implementing speech acts
24.2 understanding language strings
24.2.1 phrase-structure grammars
24.2.2 semantic analysis
24.2.3 expanding the grammar
24.3 efficient communication
24.3.1 use of context
24.3.2 use of knowledge to resolve ambiguities
24.4 natural language processing
24.5 additional readings and discussion
exercises
25 agent architectures
25.1 three-level architectures
25.2 goal arbitration
25.3 the triple-tower architecture
25.4 bootstrapping
25.5 additional readings and discussion
exercises
bibliography
index
preface
introduction
1.1 what is al?
1.2 approaches to artificial intelligence
1.3 brief history of al
1.4 plan of the book
1.5 additional readings and discussion
exercises
1 reactive machines
2 stimulus-response agents
2.1 perception and action
2.1.1 perception
2.1.2 action
2.1.3 boolean algebra
2.1.4 classes and forms of boolean functions
2.2 representing and implementing action functions
2.2.1 production systems
2.2.2 networks
2.2.3 the subsumption architecture
.2.3 additional readings and discussion
exercises
neural networks
3.1 introduction
3.2 training single tlus
3.2.1 tlu geometry
3.2.2 augmented vectors
3.2.3 gradient descent methods
3.2.4 the widrow-hoff procedure
3.2.5 the generalized delta procedure
3 . 2.6 the error-correction procedure
3.3 neural networks
3.3.1 motivation
3.3.2 notation
3.3.3 the backpropagation method
3.3.4 computing weight changes in the final layer
3.3.5 computing changes to the weights in intermediate layers
3.4 generalization, accuracy, and overfitting
3.5 additional readings and discussion
exercises
4 machine evolution
4.1 evolutionary computation
4.2 genetic programming
4.2.1 program representation in gp
4.2.2 the gp process
4.2.3 evolving a wall-following robot
4.3 additional readings and discussion
exercises
5 state machines
5.1 representing the environment by feature vectors
5.2 elman networks
5.3 iconic representations
5.4 blackboard systems
5.5 additional readings and discussion
exercises
6 robot vision
6.1 introduction
6.2 steering an automobile
6.3 two stages of robot vision
6.4 image processing
6.4.1 averaging
6.4.2 edge enhancement
6.4.3 combining edge enhancement with averaging
6.4.4 region finding
6.4.5 using image attributes other than intensity
6.5 scene analysis
6.5.1 interpreting lines and curves in the image
6.5.2 model-based vision
6.6 stereo vision and depth information
6.7 additional readings and discussion
exercises
search in state spaces
7 agents that plan
7.1 memory versus computation
7.2 state-space graphs
7.3 searching explicit state spaces
7.4 feature-based state spaces
7.5 graph notation
7.6 additional readings and discussion
exercises
8 uninformed search
8.1 formulating the state space
8.2 components of implicit state-space graphs
8.3 breadth-first search
8.4 depth-first or backtracking search
8.5 iterative deepening
8.6 additional readings and discussion
exercises
9 heuristic search
9.1 using evaluation functions
9.2 a general graph-searching algorithm
9.2.1 algorithm a*
9.2.2 admissibility of a*
9.2.3 the consistency (or monotone) condition
9.2.4 iterative-deepening a*
9.2.5 recursive best-first search
9.3 heuristic functions and search efficiency
9.4 additional readings and discussion
exercises
10 planning, acting, and learning
10.1 the sense/plan/act cycle
10.2 approximate search
10.2.1 island-driven search
10.2.2 hierarchical search
10.2.3 limited-horizon search
10.2.4 cycles
10.2.5 building reactive procedures
10.3 learning heuristic functions
10.3.1 explicit graphs
10.3.2 implicit graphs
10.4 rewards instead of goals
10.5 additional readings and discussion
exercises
11 alternative search formulations and
applications
11.1 assignment problems
11.2 constructive methods
11.3 heuristic repair
11.4 function optimization
exercises
12 adversarial search
12.1 two-agent games
12.2 the minimax procedure
12.3 the alpha-beta procedure
12.4 the search efficiency of the alpha-beta procedure
12.5 other important matters
12.6 games of chance
12.7 learning evaluation functions
12.8 additional readings and discussion
exercises
iii knowledge representation and
reasoning
13 the propositional calculus
13.1 using constraints on feature values
13.2 the language
13.3 rules of inference
13.4 definition of proof
13.5 semantics
13.5.1 interpretations
13.5.2 the propositional truth table
13.5.3 satisfiability and models
13.5.4 validity
13.5.5 equivalence
13.5.6 entailment
13.6 soundness and completeness
13.7 the psat problem
13.8 other important topics
13.8.1 language distinctions
13.8.2 metatheorems
13.8.3 associative laws
13.8.4 distributive laws
exercises
14 resolution in the propositional calculus
14.1 a new rule of inference: resolution
14.1.1 clauses as wffs
14.1.2 resolution on clauses
14.1.3 soundness of resolution
14.2 converting arbitrary wffs to conjunctions of clauses
14.3 resolution refutations
14.4 resolution refutation search strategies
14.4.1 ordering strategies
14.4.2 refinement strategies
14.5 horn clauses
exercises
15 the predicate calculus
15.1 motivation
15.2 the language and its syntax
15.3 semantics
15.3.1 worlds
15.3.2 interpretations
15.3.3 models and related notions
15.3.4 knowledge
15.4 quantification
15.5 semantics of quantifiers
15.5.1 universal quantifiers
15.5.2 existential quantifiers
15.5.3 useful equivalences
15.5.4 rules of inference
15.6 predicate calculus as a language for representing knowledge
15.6.1 conceptualizations
15.6.2 examples
15.7 additional readings and discussion
exercises
16 resolution in the predicate calculus
16.1 unification
16.2 predicate-calculus resolution
16.3 completeness and soundness
16.4 converting arbitrary wffs to clause form
16.5 using resolution to prove theorems
16. 6 answer extraction
16.7 the equality predicate
16.8 additional readings and discussion
exercises
17 knowledge based systerms
17.1 confronting the real world
17.2 reasoning using horn clauses
17.3 maintenance in dynamic knowledge bases
17.4 rule-based expert systems
17.5 rule learning
17.5.1 learning propositional calculus rules
17.5.2 learning first-order logic rules
17.5.3 explanation-based generalization
17.6 additional readings and discussion
exercises
18 representing commonsense knowledge
18.1 the commonsense world
18.1.1 what is commonsense knowledge?
18.1.2 difficulties in representing commonsense knowledge
18.1.3 the importance of commonsense knowledge
18.1.4 research areas
18.2 time
18.3knowledge representation by networks
18.3.1 taxonomic knowledge
18.3.2 semantic networks
18.3.3 nonmonotonic reasoning in semantic networks
18.3.4 frames
18.4 additional readings and discussion
exercises
19 reasoning with uncertain information
19.1 review of probability theory
19.1.1 fundamental ideas
19.1.2 conditional probabilities
19.2 probabilistic inference
19.2.1 a general method
19.2.2 conditional independence
19.3 bayes networks
19.4 patterns of inference in bayes networks
19.5 uncertain evidence
19.6 d-separation
19.7probabilistic inference in polytrees
19.7.1 evidence above
19.7.2 evidence below
19.7.3 evidence above and below
19.7.4 a numerical example
19.8 additional readings and discussion
exercises
20 learning and acting with bayes nets
20.1 learning bayes nets
20.1.1 known network structure
20.1.2 learning network structure
20.2 probabilistic inference and action
20.2.1 the general setting
20.2.2 an extended example
20.2.3 generalizing the example
20.3 additional readings and discussion
exercises
iv planning methods based on
logic
21 the situation calculus
21.1 reasoning about states and actions
21.2 some difficulties
21.2.1 frame axioms
21.2.2 qualifications
21.2.3 ramifications
21.3 generating plans
21.4 additional readings and discussion
exercises
22 planning
22.1 strips planning systems
22.1.1 describing states and goals
22.1.2 fomward search methods
22.1.3 recursive strips
22.1.4 plans with run-time conditionals
22.1.5 the sussman anomaly
22.1.6 backward search methods
22.2 plan spaces and partial-order planning
22.3 hierarchical planning
22.3.1 abstrips
22.3.2 combining hierarchical and partial-order 22 planning
22.4 learning plans
22.5 additional readings and discussion
exercises
v communication and integration
2.3 multiple agents
23.1 interacting agents
23.2 models of other agents
23.2.1 varieties of models
23.2.2 simulation strategies
23.2.3 simulated databases
23.2.4 the intentional stance
23.3 a modal logic of knowledge
23.3.1 modal operators
23.3.2 knowledge axioms
23.3.3 reasoning about other agents’ knowledge
23.3.4 predicting actions of other agents
23.4 additional readings and discussion
exercises
24 communication among agents
24.1 speech acts
24.1.1 planning speech acts
24.1.2 implementing speech acts
24.2 understanding language strings
24.2.1 phrase-structure grammars
24.2.2 semantic analysis
24.2.3 expanding the grammar
24.3 efficient communication
24.3.1 use of context
24.3.2 use of knowledge to resolve ambiguities
24.4 natural language processing
24.5 additional readings and discussion
exercises
25 agent architectures
25.1 three-level architectures
25.2 goal arbitration
25.3 the triple-tower architecture
25.4 bootstrapping
25.5 additional readings and discussion
exercises
bibliography
index
Artificial intelligence a new synthesis
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