Artificial Intelligence A New Synthesis
Intelligent 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,...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Burlington :
Elsevier Science
1998.
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| Edición: | 1st edition |
| Colección: | The Morgan Kaufmann Series in Artificial Intelligence
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009627022506719 |
Tabla de Contenidos:
- Front Cover; Artificial Intelligence: A New Synthesis; Copyright Page; Table of Contents; Preface; Chapter 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; Exercises; Part I: Reactive Machines; Chapter 2. Stimulus-Response Agents; 2.1 Perception and Action; 2.2 Representing and Implementing Action Functions; 2.3 Additional Readings and Discussion; Exercises; Chapter 3. Neural Networks; 3.1 Introduction; 3.2 Training Single TLUs; 3.3 Neural Networks
- 3.4 Generalization, Accuracy, and Overfitting3.5 Additional Readings and Discussion; Exercises; Chapter 4. Machine Evolution; 4.1 Evolutionary Computation; 4.2 Genetic Programming; 4.3 Additional Readings and Discussion; Exercises; Chapter 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; Chapter 6. Robot Vision; 6.1 Introduction; 6.2 Steering an Automobile; 6.3 Two Stages of Robot Vision; 6.4 Image Processing; 6.5 Scene Analysis
- 6.6 Stereo Vision and Depth Information6.7 Additional Readings and Discussion; Exercises; Part II: Search in State Spaces; Chapter 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; Chapter 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
- Chapter 9. Heuristic Search9.1 Using Evaluation Functions; 9.2 A General Graph-Searching Algorithm; 9.3 Heuristic Functions and Search Efficiency; 9.4 Additional Readings and Discussion; Exercises; Chapter 10. Planning, Acting, and Learning; 10.1 The Sense/Plan/Act Cycle; 10.2 Approximate Search; 10.3 Learning Heuristic Functions; 10.4 Rewards Instead of Goals; 10.5 Additional Readings and Discussion; Exercises; Chapter 11. Alternative Search Formulations and Applications; 11.1 Assignment Problems; 11.2 Constructive Methods; 11.3 Heuristic Repair; 11.4 Function Optimization; Exercises
- Chapter 12. Adversarial Search12.1 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; Part III: Knowledge Representation and Reasoning; Chapter 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; 13.6 Soundness and Completeness; 13.7 The PSAT Problem
- 13.8 Other Important Topics
