Artificial Intelligence IlluminatedJones & Bartlett Learning, 2004 - 739 страници 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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Страница vii
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Страница xix
... Genetic Programming 374 13.10 Evolutionary Programming 375 13.11 L - Systems 376 13.12 Classifier Systems 377 13.13 ... Algorithms 387 14.1 Introduction 387 14.2 Representations 388 14.3 The Algorithm 14.4 Fitness 390 389 14.5 Crossover ...
... Genetic Programming 374 13.10 Evolutionary Programming 375 13.11 L - Systems 376 13.12 Classifier Systems 377 13.13 ... Algorithms 387 14.1 Introduction 387 14.2 Representations 388 14.3 The Algorithm 14.4 Fitness 390 389 14.5 Crossover ...
Страница xx
... Genetic Algorithms Work 396 14.9.1 14.9.2 Schemata 397 How Reproduction Affects Schemata 399 14.9.3 How Mutation and Crossover Affect Schemata 401 14.9.4 The Building - Block Hypothesis 403 14.9.5 Deception 404 14.10 Messy Genetic ...
... Genetic Algorithms Work 396 14.9.1 14.9.2 Schemata 397 How Reproduction Affects Schemata 399 14.9.3 How Mutation and Crossover Affect Schemata 401 14.9.4 The Building - Block Hypothesis 403 14.9.5 Deception 404 14.10 Messy Genetic ...
Страница 126
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Страница 131
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Съдържание
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 | |
Често срещани думи и фрази
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