Artificial Intelligence IlluminatedJones & Bartlett Learning, 2004 - 739 Seiten Artificial Intelligence Illuminated presents an overview of the background and history of artificial intelligence, emphasizing its importance in today's society and potential for the future. The book covers a range of AI techniques, algorithms, and methodologies, including game playing, intelligent agents, machine learning, genetic algorithms, and Artificial Life. Material is presented in a lively and accessible manner and the author focuses on explaining how AI techniques relate to and are derived from natural systems, such as the human brain and evolution, and explaining how the artificial equivalents are used in the real world. Each chapter includes student exercises and review questions, and a detailed glossary at the end of the book defines important terms and concepts highlighted throughout the text. |
Im Buch
Seite xvii
... Chapter 10 Introduction to Machine Learning 267 10.1 Introduction 267 10.2 Training 268 10.3 Rote Learning 270 10.4 ... Chapter Summary 286 10.17 Review Questions 287 10.18 Exercises 288 10.19 Further Reading 288 Chapter 11 Neural ...
... Chapter 10 Introduction to Machine Learning 267 10.1 Introduction 267 10.2 Training 268 10.3 Rote Learning 270 10.4 ... Chapter Summary 286 10.17 Review Questions 287 10.18 Exercises 288 10.19 Further Reading 288 Chapter 11 Neural ...
Seite xviii
... Chapter Summary 323 11.9 Review Questions 324 11.10 Exercises 325 11.11 Further Reading 326 Chapter 12 Probabilistic Reasoning and Bayesian Belief Networks 327 12.1 Introduction 327 12.2 Probabilistic Reasoning 328 12.3 Joint ...
... Chapter Summary 323 11.9 Review Questions 324 11.10 Exercises 325 11.11 Further Reading 326 Chapter 12 Probabilistic Reasoning and Bayesian Belief Networks 327 12.1 Introduction 327 12.2 Probabilistic Reasoning 328 12.3 Joint ...
Seite xix
... Chapter Summary 382 13.15 Review Questions 382 13.16 Further Reading 383 Chapter 14 Genetic Algorithms 387 14.1 Introduction 387 14.2 Representations 388 14.3 The Algorithm 14.4 Fitness 390 389 14.5 Crossover 390 14.6 Mutation 392 14.7 ...
... Chapter Summary 382 13.15 Review Questions 382 13.16 Further Reading 383 Chapter 14 Genetic Algorithms 387 14.1 Introduction 387 14.2 Representations 388 14.3 The Algorithm 14.4 Fitness 390 389 14.5 Crossover 390 14.6 Mutation 392 14.7 ...
Seite xx
... Chapter Summary 414 14.17 Review Questions 415 14.18 Exercises 416 14.19 Further Reading 417 PART 5 Planning 419 Chapter 15 Introduction to Planning 421 15.1 Introduction 421 15.2 Planning as Search 423 15.3 Situation Calculus 426 15.4 ...
... Chapter Summary 414 14.17 Review Questions 415 14.18 Exercises 416 14.19 Further Reading 417 PART 5 Planning 419 Chapter 15 Introduction to Planning 421 15.1 Introduction 421 15.2 Planning as Search 423 15.3 Situation Calculus 426 15.4 ...
Seite xxi
... Chapter Summary 459 16.15 Review Questions 460 16.16 Exercises 461 16.17 Further Reading 461 PART 6 Advanced Topics 463 Chapter 17 Advanced Knowledge Representation 465 17.1 Introduction 465 17.2 Representations and Semantics 468 17.3 ...
... Chapter Summary 459 16.15 Review Questions 460 16.16 Exercises 461 16.17 Further Reading 461 PART 6 Advanced Topics 463 Chapter 17 Advanced Knowledge Representation 465 17.1 Introduction 465 17.2 Representations and Semantics 468 17.3 ...
Inhalt
Contents | 1 |
Uses and Limitations | 19 |
Knowledge Representation | 27 |
Search | 69 |
Advanced Search | 117 |
Game Playing | 143 |
Knowledge Representation and Automated | 173 |
Inference and Resolution for Problem Solving | 209 |
Genetic Algorithms | 387 |
Planning | 419 |
Planning Methods | 433 |
Advanced Topics | 463 |
Fuzzy Reasoning | 503 |
Intelligent Agents | 543 |
Understanding Language | 571 |
Machine Vision | 605 |
Rules and Expert Systems | 241 |
Machine Learning | 265 |
Neural Networks | 291 |
Probabilistic Reasoning and Bayesian Belief | 327 |
Learning through Emergent | 363 |
Glossary | 633 |
Bibliography | 697 |
719 | |
Häufige Begriffe und Wortgruppen
able actions agents alpha-beta pruning analysis applied architecture Artificial Intelligence Bayesian behavior block branching factor breadth-first search calculate Chapter chess chromosome classifier complex consider crossover current_node database decision tree defined depth-first search described determine edge edited examine example expert system Explain expression fact false frame fuzzy logic fuzzy sets game tree genetic algorithms goal node goal tree grammar Hence heuristic human hypothesis idea information retrieval input involves knowledge layer leaf nodes learning match means membership functions Minimax move MoveOnto natural language processing neural networks neurons nonmonotonic noun object operator optimal output path perceptron position possible Press probability PROLOG propositional logic queue reasoning represent representation robot root node rules schema search method search space search tree semantic sentence set of clauses shown in Figure simple situation solution Springer Verlag symbols techniques theorem tion training data true truth table variables vector words X₁