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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Seite vii
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Seite xiv
... Reasoning 173 Chapter 7 Propositional and Predicate Logic 175 7.1 Introduction 175 7.2 What Is Logic ? 176 7.3 Why Logic Is Used in Artificial Intelligence 176 7.4 Logical Operators 177 7.5 Translating between English and Logic Notation ...
... Reasoning 173 Chapter 7 Propositional and Predicate Logic 175 7.1 Introduction 175 7.2 What Is Logic ? 176 7.3 Why Logic Is Used in Artificial Intelligence 176 7.4 Logical Operators 177 7.5 Translating between English and Logic Notation ...
Seite xv
... Reasoning 201 7.20 Modal Logics and Possible Worlds 203 7.20.1 Reasoning in Modal Logic 204 7.21 Dealing with Change 205 7.22 Chapter Summary 205 7.23 Review Questions 205 7.24 Exercises 206 7.25 Further Reading 208 Chapter 8 Inference ...
... Reasoning 201 7.20 Modal Logics and Possible Worlds 203 7.20.1 Reasoning in Modal Logic 204 7.21 Dealing with Change 205 7.22 Chapter Summary 205 7.23 Review Questions 205 7.24 Exercises 206 7.25 Further Reading 208 Chapter 8 Inference ...
Seite xviii
... Reasoning and Bayesian Belief Networks 327 12.1 Introduction 327 12.2 Probabilistic Reasoning 328 12.3 Joint Probability Distributions 330 12.4 Bayes ' Theorem 330 12.4.1 Example : Medical Diagnosis 331 12.4.2 Example : Witness ...
... Reasoning and Bayesian Belief Networks 327 12.1 Introduction 327 12.2 Probabilistic Reasoning 328 12.3 Joint Probability Distributions 330 12.4 Bayes ' Theorem 330 12.4.1 Example : Medical Diagnosis 331 12.4.2 Example : Witness ...
Seite xxii
... Reasoning 476 17.6.1 Nonmonotonic Logic with the Modal Operator 477 17.6.2 Default Reasoning 477 17.6.3 Truth Maintenance Systems 478 17.6.4 Closed - World Assumption 480 17.6.5 The Ramification Problem 480 17.6.6 Circumscription 480 ...
... Reasoning 476 17.6.1 Nonmonotonic Logic with the Modal Operator 477 17.6.2 Default Reasoning 477 17.6.3 Truth Maintenance Systems 478 17.6.4 Closed - World Assumption 480 17.6.5 The Ramification Problem 480 17.6.6 Circumscription 480 ...
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