Artificial Intelligence IlluminatedArtificial 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. |
Contents
Contents | 1 |
Uses and Limitations | 19 |
Chapter 3 | 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 analysis applied approach Artificial Intelligence block calculate called carried Chapter chromosome classifier clauses clearly complex consider consists contains defined described determine developed discussed edge edited examine example expert system Explain expression fact false Figure fitness frame function fuzzy genetic algorithms give given goal graph Hence heuristic human idea important input Introduction involves knowledge known language layer learning logic look match means method move natural neural networks neurons node Note object operator output particular path planning play position possible Press probability problem produce propositional prove reasoning represent representation result robot rules search method search tree sentence shown in Figure shows simple situation solution solve space tion training data tree true usually variables weights