ARTIFICIAL INTELLIGENCE： A MODERN APPROACH 4/E (GE)
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The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence
The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
I Artificial Intelligence
1 Introduction
2 Intelligent Agents
II Problem-solving
3 Solving Problems by Searching
4 Search in Complex Environments
5 Constraint Satisfaction Problems
6 Adversarial Search and Games
III Knowledge, reasoning, and planning
7 Logical Agents
8 First-Order Logic
9 Inference in First-Order Logic
10 Knowledge Representation
11 Automated Planning
IV Uncertain knowledge and reasoning
12 Quantifying Uncertainty
13 Probabilistic Reasoning
14 Probabilistic Reasoning over Time
15 Making Simple Decisions
16 Making Complex Decisions
17 Multiagent Decision Making
18 Probabilistic Programming
V Machine Learning
19 Learning from Examples
20 Knowledge in Learning
21 Learning Probabilistic Models
22 Deep Learning
23 Reinforcement Learning
VI Communicating, perceiving, and acting
24 Natural Language Processing
25 Deep Learning for Natural Language Processing
26 Robotics
27 Computer Vision
VII Conclusions
28 Philosophy, Ethics, and Safety of AI
29 The Future of AI
Appendix A: Mathematical Background
Appendix B: Notes on Languages and Algorithms
Bibliography
Index
The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
I Artificial Intelligence
1 Introduction
2 Intelligent Agents
II Problem-solving
3 Solving Problems by Searching
4 Search in Complex Environments
5 Constraint Satisfaction Problems
6 Adversarial Search and Games
III Knowledge, reasoning, and planning
7 Logical Agents
8 First-Order Logic
9 Inference in First-Order Logic
10 Knowledge Representation
11 Automated Planning
IV Uncertain knowledge and reasoning
12 Quantifying Uncertainty
13 Probabilistic Reasoning
14 Probabilistic Reasoning over Time
15 Making Simple Decisions
16 Making Complex Decisions
17 Multiagent Decision Making
18 Probabilistic Programming
V Machine Learning
19 Learning from Examples
20 Knowledge in Learning
21 Learning Probabilistic Models
22 Deep Learning
23 Reinforcement Learning
VI Communicating, perceiving, and acting
24 Natural Language Processing
25 Deep Learning for Natural Language Processing
26 Robotics
27 Computer Vision
VII Conclusions
28 Philosophy, Ethics, and Safety of AI
29 The Future of AI
Appendix A: Mathematical Background
Appendix B: Notes on Languages and Algorithms
Bibliography
Index
目錄
I Artificial Intelligence
1 Introduction
2 Intelligent Agents
II Problem-solving
3 Solving Problems by Searching
4 Search in Complex Environments
5 Constraint Satisfaction Problems
6 Adversarial Search and Games
III Knowledge, reasoning, and planning
7 Logical Agents
8 First-Order Logic
9 Inference in First-Order Logic
10 Knowledge Representation
11 Automated Planning
IV Uncertain knowledge and reasoning
12 Quantifying Uncertainty
13 Probabilistic Reasoning
14 Probabilistic Reasoning over Time
15 Making Simple Decisions
16 Making Complex Decisions
17 Multiagent Decision Making
18 Probabilistic Programming
V Machine Learning
19 Learning from Examples
20 Knowledge in Learning
21 Learning Probabilistic Models
22 Deep Learning
23 Reinforcement Learning
VI Communicating, perceiving, and acting
24 Natural Language Processing
25 Deep Learning for Natural Language Processing
26 Robotics
27 Computer Vision
VII Conclusions
28 Philosophy, Ethics, and Safety of AI
29 The Future of AI
Appendix A: Mathematical Background
Appendix B: Notes on Languages and Algorithms
Bibliography
Index
1 Introduction
2 Intelligent Agents
II Problem-solving
3 Solving Problems by Searching
4 Search in Complex Environments
5 Constraint Satisfaction Problems
6 Adversarial Search and Games
III Knowledge, reasoning, and planning
7 Logical Agents
8 First-Order Logic
9 Inference in First-Order Logic
10 Knowledge Representation
11 Automated Planning
IV Uncertain knowledge and reasoning
12 Quantifying Uncertainty
13 Probabilistic Reasoning
14 Probabilistic Reasoning over Time
15 Making Simple Decisions
16 Making Complex Decisions
17 Multiagent Decision Making
18 Probabilistic Programming
V Machine Learning
19 Learning from Examples
20 Knowledge in Learning
21 Learning Probabilistic Models
22 Deep Learning
23 Reinforcement Learning
VI Communicating, perceiving, and acting
24 Natural Language Processing
25 Deep Learning for Natural Language Processing
26 Robotics
27 Computer Vision
VII Conclusions
28 Philosophy, Ethics, and Safety of AI
29 The Future of AI
Appendix A: Mathematical Background
Appendix B: Notes on Languages and Algorithms
Bibliography
Index
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