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 87–ի 1-ից 5-ը:
Էջ vii
Ներեցեք, այս էջի պարունակությունն արգելված է:.
Ներեցեք, այս էջի պարունակությունն արգելված է:.
Էջ x
... Representation 27 3.1 Introduction 27 3.2 The Need for a Good Representation 28 3.3 Semantic Nets 29 3.4 Inheritance 31 3.5 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 ...
... Representation 27 3.1 Introduction 27 3.2 The Need for a Good Representation 28 3.3 Semantic Nets 29 3.4 Inheritance 31 3.5 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 ...
Էջ xiv
... Representation and Automated 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 ...
... Representation and Automated 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 ...
Էջ xvi
... Representation 242 9.3 Rule - Based Systems 243 9.3.1 Forward Chaining 244 9.3.2 Conflict Resolution 246 9.3.3 Meta Rules 247 9.3.4 Backward Chaining 248 9.3.5 Comparing Forward and Backward Chaining 249 9.4 Rule - Based Expert Systems ...
... Representation 242 9.3 Rule - Based Systems 243 9.3.1 Forward Chaining 244 9.3.2 Conflict Resolution 246 9.3.3 Meta Rules 247 9.3.4 Backward Chaining 248 9.3.5 Comparing Forward and Backward Chaining 249 9.4 Rule - Based Expert Systems ...
Էջ xxi
... Representation 465 17.1 Introduction 465 17.2 Representations and Semantics 468 17.3 The Blackboard Architecture 469 17.3.1 Implementation 471 17.3.2 HEARSAY Contents xxi.
... Representation 465 17.1 Introduction 465 17.2 Representations and Semantics 468 17.3 The Blackboard Architecture 469 17.3.1 Implementation 471 17.3.2 HEARSAY Contents xxi.
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