Artificial Intelligence IlluminatedJones & Bartlett Learning, 2004 - 739 pages 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
Results 1-5 of 83
Page x
... Example 1 : Missionaries and Cannibals 3.9.2 Improving the Representation 49 3.9.3 Example 2 : The Traveling Salesman 50 3.9.4 Example 3 : The Towers of Hanoi 54 3.9.5 Example 4 : Describe and Match 56 3.10 Combinatorial Explosion 57 ...
... Example 1 : Missionaries and Cannibals 3.9.2 Improving the Representation 49 3.9.3 Example 2 : The Traveling Salesman 50 3.9.4 Example 3 : The Towers of Hanoi 54 3.9.5 Example 4 : Describe and Match 56 3.10 Combinatorial Explosion 57 ...
Page xi
... Example 1 : Map Coloring Example 2 : Proving Theorems Example 3 : Parsing Sentences 63 Example 4 : Games 3.13 Chapter Summary 64 3.14 Review Questions 65 3.15 Exercises 65 3.16 Further Reading 66 PART 2 Search 69 Chapter 4 Search ...
... Example 1 : Map Coloring Example 2 : Proving Theorems Example 3 : Parsing Sentences 63 Example 4 : Games 3.13 Chapter Summary 64 3.14 Review Questions 65 3.15 Exercises 65 3.16 Further Reading 66 PART 2 Search 69 Chapter 4 Search ...
Page xii
... Example : The Knapsack Problem 111 4.17 Chapter Summary 113 4.18 Review Questions 114 4.19 Exercises 115 4.20 Further Reading 116 Chapter 5 Advanced Search 117 5.1 Introduction 117 5.2 Constraint Satisfaction Search 118 5.3 Forward ...
... Example : The Knapsack Problem 111 4.17 Chapter Summary 113 4.18 Review Questions 114 4.19 Exercises 115 4.20 Further Reading 116 Chapter 5 Advanced Search 117 5.1 Introduction 117 5.2 Constraint Satisfaction Search 118 5.3 Forward ...
Page xv
... Example 1 193 7.11.9 Example 2 194 7.11.10 Example 3 194 7.11.11 Example 4 195 7.12 The Deduction Theorem 7.13 Predicate Calculus 196 7.13.1 Syntax 196 195 7.13.2 Relationships between " and $ 197 7.13.3 Functions 199 7.14 First - Order ...
... Example 1 193 7.11.9 Example 2 194 7.11.10 Example 3 194 7.11.11 Example 4 195 7.12 The Deduction Theorem 7.13 Predicate Calculus 196 7.13.1 Syntax 196 195 7.13.2 Relationships between " and $ 197 7.13.3 Functions 199 7.14 First - Order ...
Page xvi
... Example of Skolemization 221 222 8.6.2 Second Example of Skolemization Unification 222 8.6.3 8.6.4 Most General Unifiers 224 8.6.5 Unification Algorithm 224 8.6.6 Unification Example 225 8.7 Resolution Algorithm 226 8.8 Horn Clauses and ...
... Example of Skolemization 221 222 8.6.2 Second Example of Skolemization Unification 222 8.6.3 8.6.4 Most General Unifiers 224 8.6.5 Unification Algorithm 224 8.6.6 Unification Example 225 8.7 Resolution Algorithm 226 8.8 Horn Clauses and ...
Contents
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 |
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