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. |
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... Frames 32 3.5.1 Why Are Frames Useful ? 34 3.5.2 Inheritance 34 3.5.3 Slots as Frames 35 3.5.4 Multiple Inheritance 36 3.5.5 Procedures 37 3.5.6 Demons 38 3.5.7 Implementation 38 3.5.8 Combining Frames with Rules 40 3.5.9 ...
... Frames 32 3.5.1 Why Are Frames Useful ? 34 3.5.2 Inheritance 34 3.5.3 Slots as Frames 35 3.5.4 Multiple Inheritance 36 3.5.5 Procedures 37 3.5.6 Demons 38 3.5.7 Implementation 38 3.5.8 Combining Frames with Rules 40 3.5.9 ...
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... 421 15.2 Planning as Search 423 15.3 Situation Calculus 426 15.4 The Frame Problem 427 15.5 Means - Ends Analysis 428 15.6 Chapter Summary 430 15.7 Review Questions 431 15.8 Exercises 431 15.9 Further Reading XX Contents.
... 421 15.2 Planning as Search 423 15.3 Situation Calculus 426 15.4 The Frame Problem 427 15.5 Means - Ends Analysis 428 15.6 Chapter Summary 430 15.7 Review Questions 431 15.8 Exercises 431 15.9 Further Reading XX Contents.
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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