Artificial Intelligence
A New Synthesis
- 1st Edition - April 1, 1998
- Imprint: Morgan Kaufmann
- Author: Nils J. Nilsson
- Language: English
- Hardback ISBN:9 7 8 - 1 - 5 5 8 6 0 - 4 6 7 - 4
- eBook ISBN:9 7 8 - 0 - 0 8 - 0 4 9 9 4 5 - 1
Intelligent agents are employed as the central characters in this new introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive ho… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteIntelligent agents are employed as the central characters in this new introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents. The book provides a refreshing and motivating new synthesis of the field by one of AI's master expositors and leading researchers. Artificial Intelligence: A New Synthesis takes the reader on a complete tour of this intriguing new world of AI.
- An evolutionary approach provides a unifying theme
- Thorough coverage of important AI ideas, old and new
- Frequent use of examples and illustrative diagrams
- Extensive coverage of machine learning methods throughout the text
- Citations to over 500 references
- Comprehensive index
1 Introduction 1.1 What is AI? 1.2 Approaches to Artificial Intelligence 1.3 Brief History of AI 1.4 Plan of the Book 1.5 Additional Readings and Discussion I Reactive Machines 2 Stimulus-Response Agents2.1 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 3 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 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 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 6 Robot Vision 6.1 Introduction 6.2 Steering a Van 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 6.7 Additional Readings and Discussion II Search in State Spaces7 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 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 Bracktracking Search 8.5 Iterative Deepening 8.6 Additional Readings and Discussion 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 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 11 Alternative Search Formulations and Applications 11.1 Assignment Problems 11.2 Constructive Methods 11.3 Heuristic Repair 11.4 Function Optimization 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 III Knowledge Representation and Reasoning13 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 14 Resolution in The Propositional Calculus 14.1 A New Rule of Inference: Resolution 14.1.1 Clauses as wwf 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 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 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 17 Knowledge-Based Systems 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 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 18.3 Knowledge 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 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-Seperation 19.7 Probabilistic 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 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 IV Planning Method Based on Logic21 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 Reading and Discussion 22 Planning 22.1 STRIPS Planning Systems 22.1.1 Describing States and Goals 22.1.2 Forward Search Methods 22.1.3 Recursive STRIPS 22.1.4 Plans with Runtime 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 Planning 22.4 Learning Plans' 22.5 Additional Readings and Discussion V Communication and Integration23 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 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 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
- Edition: 1
- Published: April 1, 1998
- Imprint: Morgan Kaufmann
- Language: English
- Hardback ISBN: 9781558604674
- eBook ISBN: 9780080499451
NN
Nils J. Nilsson
Nils J. Nilsson's long and rich research career has contributed much to AI. He has written many books, including the classic Principles of Artificial Intelligence. Dr. Nilsson is Kumagai Professor of Engineering, Emeritus, at Stanford University. He has served on the editorial boards of Artificial Intelligence and Machine Learning and as an Area Editor for the Journal of the Association for Computing Machinery. Former Chairman of the Department of Computer Science at Stanford, and former Director of the SRI Artificial Intelligence Center, he is also a past president and Fellow of the American Association for Artificial Intelligence.
Affiliations and expertise
Stanford UniversityRead Artificial Intelligence on ScienceDirect