Artificial Intelligence: A Modern Approach, 4th Edition

学術書籍  >  理工学  >  知能情報学  > 

Artificial Intelligence: A Modern Approach, 4th Edition





Artificial Intelligence: A Modern Approach, 4th Edition
Russell & Norvig人工知能 エージェントアプローチ, 第4版
著者・編者 Stuart Russell, S. & Norvig, Peter
出版社 Pearson
発行年/月 2020年5月   
装丁 Hardcover
ページ数 1136 ページ
ISBN 978-0-13-461099-3
発送予定 在庫あり 1-2営業日内に発送致します



2010年の第3版より待望の改訂版、人工知能:エージェントアプローチ第4版は、人工知能(AI)の分野をより広く、より深く探求します。 改訂版では最新のテクノロジーについての情報提供、機械学習、ディープラーニング、転移学習、マルチエージェントシステム、ロボット工学、自然言語処理、因果関係、確率的プログラミング、プライバシー、公平性、安全なAIについての新しい、または拡張されたカバレッジを提供します。



New to This Edition

・New chapters feature expanded coverage of probabilistic programming (Ch. 15); multiagent decision making (Ch. 18 with Michael Wooldridge); deep learning (Ch. 21 with Ian Goodfellow); and deep learning for natural language processing (Ch. 24 with Jacob Devlin and Mei-Wing Chang).
・Increased coverage of machine learning.
・Significantly updated material on robotics includes robots that interact with humans and the application of reinforcement learning to robotics.
・New section on causality by Judea Pearl.
・New sections on Monte Carlo search for games and robotics.
・New sections on transfer learning for deep learning in general and for natural language
・New sections on privacy, fairness, the future of work, and safe AI.
・Extensive coverage of recent advances in AI applications.
・Revised coverage of computer vision, natural language understanding, and speech recognition reflect the impact of deep learning methods on these fields.



Part I: Artificial Intelligence
1. Introduction
2. Intelligent Agents

Part II: Problem Solving
3. Solving Problems by Searching
4. Search in Complex Environments
5. Adversarial Search and Games
6. Constraint Satisfaction Problems

Part III: Knowledge and Reasoning
7. Logical Agents
8. First-Order Logic
9. Inference in First-Order Logic
10. Knowledge Representation
11. Automated Planning
12. Quantifying Uncertainty

Part IV: Uncertain Knowledge and Reasoning
13. Probabilistic Reasoning
14. Probabilistic Reasoning over Time
15. Probabilistic Programming
16. Making Simple Decisions
17. Making Complex Decisions

Part V: Learning
18. Multiagent Decision Making
19. Learning from Examples
20. Learning Probabilistic Models
21. Deep Learning

Part VI: Communicating, Perceiving, and Acting
22. Reinforcement Learning
23. Natural Language Processing
24. Deep Learning for Natural Language Processing
25. Robotics

Part VII: Conclusions
26. Philosophy and Ethics of AI
27. The Future of AI