Artificial Intelligence IlluminatedJones & Bartlett Learning, 2004 - 739 էջ 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. |
From inside the book
Արդյունքներ 83–ի 1-ից 5-ը:
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Ներեցեք, այս էջի պարունակությունն արգելված է:.
Ներեցեք, այս էջի պարունակությունն արգելված է:.
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... 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 7.6 Truth Tables 181 7.6.1 Not 181 7.6.2 And 182 ...
... 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 7.6 Truth Tables 181 7.6.1 Not 181 7.6.2 And 182 ...
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... Logic 199 7.15 Soundness 200 7.16 Completeness 200 7.17 Decidability 200 7.18 Monotonicity 201 7.19 Abduction and Inductive Reasoning 201 7.20 Modal Logics and Possible Worlds 203 7.20.1 Reasoning in Modal Logic 204 7.21 Dealing with ...
... Logic 199 7.15 Soundness 200 7.16 Completeness 200 7.17 Decidability 200 7.18 Monotonicity 201 7.19 Abduction and Inductive Reasoning 201 7.20 Modal Logics and Possible Worlds 203 7.20.1 Reasoning in Modal Logic 204 7.21 Dealing with ...
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... 503 18.2 Bivalent and Multivalent Logics 504 18.3 Linguistic Variables 504 18.4 Fuzzy Sets 505 18.4.1 Fuzzy Set Membership Functions 507 18.4.2 Fuzzy Set Operators 508 18.4.3 Hedges 510 18.5 Fuzzy Logic 511 18.6 Fuzzy Logic xxii Contents.
... 503 18.2 Bivalent and Multivalent Logics 504 18.3 Linguistic Variables 504 18.4 Fuzzy Sets 505 18.4.1 Fuzzy Set Membership Functions 507 18.4.2 Fuzzy Set Operators 508 18.4.3 Hedges 510 18.5 Fuzzy Logic 511 18.6 Fuzzy Logic xxii Contents.
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Ben Coppin. 18.4.3 Hedges 510 18.5 Fuzzy Logic 511 18.6 Fuzzy Logic as Applied to Traditional Logical Paradoxes 515 18.7 Fuzzy Rules 516 18.8 Fuzzy Inference 516 18.9 Fuzzy Expert Systems 522 18.9.1 Defining the Fuzzy Sets 523 18.9.2 ...
Ben Coppin. 18.4.3 Hedges 510 18.5 Fuzzy Logic 511 18.6 Fuzzy Logic as Applied to Traditional Logical Paradoxes 515 18.7 Fuzzy Rules 516 18.8 Fuzzy Inference 516 18.9 Fuzzy Expert Systems 522 18.9.1 Defining the Fuzzy Sets 523 18.9.2 ...
Բովանդակություն
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 | |
Common terms and phrases
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