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. |
Im Buch
Ergebnisse 1-5 von 51
Seite x
... Implementation 38 3.5.8 Combining Frames with Rules 40 3.5.9 Representational Adequacy 40 3.6 Object - Oriented Programming 41 3.7 Search Spaces 42 3.8 Semantic Trees 44 3.9 Search Trees 46 3.9.1 Example 1 : Missionaries and Cannibals ...
... Implementation 38 3.5.8 Combining Frames with Rules 40 3.5.9 Representational Adequacy 40 3.6 Object - Oriented Programming 41 3.7 Search Spaces 42 3.8 Semantic Trees 44 3.9 Search Trees 46 3.9.1 Example 1 : Missionaries and Cannibals ...
Seite xi
... Implementing Depth - First and Breadth - First Search 4.10 Example : Web Spidering 88 4.11 Depth - First Iterative Deepening 88 4.12 Using Heuristics for Search 90 4.12.1 Informed and Uninformed Methods 4.12.2 Choosing a Good Heuristic ...
... Implementing Depth - First and Breadth - First Search 4.10 Example : Web Spidering 88 4.11 Depth - First Iterative Deepening 88 4.12 Using Heuristics for Search 90 4.12.1 Informed and Uninformed Methods 4.12.2 Choosing a Good Heuristic ...
Seite xiii
... Implementation 155 6.5 Checkers 159 6.5.1 Chinook 160 6.5.2 Chinook's Databases 161 6.5.3 Chinook's Evaluation Function 162 6.5.4 Forward Pruning 163 6.5.5 Limitations of Minimax 163 6.5.6 Blondie 24 164 6.6 Chess 164 6.7 Go 165 6.7.1 ...
... Implementation 155 6.5 Checkers 159 6.5.1 Chinook 160 6.5.2 Chinook's Databases 161 6.5.3 Chinook's Evaluation Function 162 6.5.4 Forward Pruning 163 6.5.5 Limitations of Minimax 163 6.5.6 Blondie 24 164 6.6 Chess 164 6.7 Go 165 6.7.1 ...
Seite xxi
... Implementation of STRIPS 437 16.2.4 Example : STRIPS 438 16.2.5 Example : STRIPS and Resolution 441 16.3 The Sussman Anomaly 443 16.4 Partial Order Planning 444 16.5 The Principle of Least Commitment 447 16.6 Propositional Planning 448 ...
... Implementation of STRIPS 437 16.2.4 Example : STRIPS 438 16.2.5 Example : STRIPS and Resolution 441 16.3 The Sussman Anomaly 443 16.4 Partial Order Planning 444 16.5 The Principle of Least Commitment 447 16.6 Propositional Planning 448 ...
Seite xxii
Ben Coppin. 17.3 The Blackboard Architecture 469 17.3.1 Implementation 471 17.3.2 HEARSAY 472 17.4 Scripts 472 17.5 Copycat Architecture 474 17.6 Nonmonotonic Reasoning 476 17.6.1 Nonmonotonic Logic with the Modal Operator 477 17.6.2 ...
Ben Coppin. 17.3 The Blackboard Architecture 469 17.3.1 Implementation 471 17.3.2 HEARSAY 472 17.4 Scripts 472 17.5 Copycat Architecture 474 17.6 Nonmonotonic Reasoning 476 17.6.1 Nonmonotonic Logic with the Modal Operator 477 17.6.2 ...
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