Artificial Intelligence: A Modern Approach, Global Edition
Produktdetaljer
Biographical note
Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California, Berkeley, where he is a Professor and former Chair of Computer Science, Director of the Centre for Human-Compatible AI, and holder of the Smith–Zadeh Chair in Engineering.
In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was co-winner of the Computers and Thought Award. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science, and Honorary Fellow of Wadham College, Oxford, and an Andrew Carnegie Fellow. He held the Chaire Blaise Pascal in Paris from 2012 to 2014. He has published over 300 papers on a wide range of topics in artificial intelligence. His other books include: The Use of Knowledge in Analogy and Induction, Do the Right Thing: Studies in Limited Rationality (with Eric Wefald), and Human Compatible: Artificial Intelligence and the Problem of Control.
Peter Norvig is currently Director of Research at Google, Inc., and was the director responsible for the core Web search algorithms from 2002 to 2005. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Previously, he was head of the Computational Sciences Division at NASA Ames Research Center, where he oversaw NASA's research and development in artificial intelligence and robotics, and chief scientist at Junglee, where he helped develop one of the first Internet information extraction services. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from the University of California at Berkeley.
He received the Distinguished Alumni and Engineering Innovation awards from Berkeley and the Exceptional Achievement Medal from NASA. He has been a professor at the University of Southern California and are research faculty member at Berkeley. His other books are: Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX.
The two authors shared the inaugural AAAI/EAAI Outstanding Educator award in 2016.
Thelong-anticipated revision of ArtificialIntelligence: A Modern Approach explores the full breadth and depth of the field of artificialintelligence (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 coverageof machine learning, deep learning, transfer learning, multi agent systems,robotics, natural language processing, causality, probabilistic programming,privacy, fairness, and safe AI.
- Introduction
- What Is AI?
- The Foundations of Artificial Intelligence
- The History of Artificial Intelligence
- The State of the Art
- Risks and Benefits of AI
- Intelligent Agents
- Agents and Environments
- Good Behavior: The Concept of Rationality
- The Nature of Environments
- The Structure of Agents
- Chapter II Problem Solving
- Solving Problems by Searching
- Problem-Solving Agents
- Example Problems
- Search Algorithms
- Uninformed Search Strategies
- Informed (Heuristic) Search Strategies
- Heuristic Functions
- Search in Complex Environments
- Local Search and Optimization Problems
- Local Search in Continuous Spaces
- Search with Nondeterministic Actions
- Search in Partially Observable Environments
- Online Search Agents and Unknown Environments
- Constraint Satisfaction Problems
- Defining Constraint Satisfaction Problems
- Constraint Propagation: Inference in CSPs
- Backtracking Search for CSPs
- Local Search for CSPs
- The Structure of Problems
- Adversarial Search and Games
- Game Theory
- Optimal Decisions in Games
- Heuristic Alpha--Beta Tree Search
- Monte Carlo Tree Search
- Stochastic Games
- Partially Observable Games
- Limitations of Game Search Algorithms
- Chapter III Knowledge, Reasoning and Planning
- Logical Agents
- Knowledge-Based Agents
- The Wumpus World
- Logic
- Propositional Logic: A Very Simple Logic
- Propositional Theorem Proving
- Effective Propositional Model Checking
- Agents Based on Propositional Logic
- First-Order Logic
- Representation Revisited
- Syntax and Semantics of First-Order Logic
- Using First-Order Logic
- Knowledge Engineering in First-Order Logic
- Inference in First-Order Logic
- Propositional vs. First-Order Inference
- Unification and First-Order Inference
- Forward Chaining
- Backward Chaining
- Resolution
- Knowledge Representation
- Ontological Engineering
- Categories and Objects
- Events
- Mental Objects and Modal Logic
- for Categories
- Reasoning with Default Information
- Automated Planning
- Definition of Classical Planning
- Algorithms for Classical Planning
- Heuristics for Planning
- Hierarchical Planning
- Planning and Acting in Nondeterministic Domains
- Time, Schedules, and Resources
- Analysis of Planning Approaches
- Chapter IV Uncertain Knowledge and Reasoning
- Quantifying Uncertainty
- Acting under Uncertainty
- Basic Probability Notation
- Inference Using Full Joint Distributions
- Independence 12.5 Bayes' Rule and Its Use
- Naive Bayes Models
- The Wumpus World Revisited
- Probabilistic Reasoning
- Representing Knowledge in an Uncertain Domain
- The Semantics of Bayesian Networks
- Exact Inference in Bayesian Networks
- Approximate Inference for Bayesian Networks
- Causal Networks
- Probabilistic Reasoning over Time
- Time and Uncertainty
- Inference in Temporal Models
- Hidden Markov Models
- Kalman Filters
- Dynamic Bayesian Networks
- Making Simple Decisions
- Combining Beliefs and Desires under Uncertainty
- The Basis of Utility Theory
- Utility Functions
- Multiattribute Utility Functions
- Decision Networks
- The Value of Information
- Unknown Preferences
- Making Complex Decisions
- Sequential Decision Problems
- Algorithms for MDPs
- Bandit Problems
- Partially Observable MDPs
- Algorithms for Solving POMDPs
- Multiagent Decision Making
- Properties of Multiagent Environments
- Non-Cooperative Game Theory
- Cooperative Game Theory
- Making Collective Decisions
- Probabilistic Programming
- Relational Probability Models
- Open-Universe Probability Models
- Keeping Track of a Complex World
- Programs as Probability Models
- Chapter V Machine Learning
- Learning from Examples
- Forms of Leaming
- Supervised Learning .
- Learning Decision Trees .
- Model Selection and Optimization
- The Theory of Learning
- Linear Regression and Classification
- Nonparametric Models
- Ensemble Learning
- Developing Machine Learning Systen
- Knowledge in Learning
- A Logical Formulation of Learning
- Knowledge in Learning
- Exmplanation-Based Leaening
- Learning Using Relevance Information
- Inductive Logic Programming
- Learning Probabilistic Models
- Statistical Learning
- Learning with Complete Data
- Learning with Hidden Variables: The EM Algorithm
- Deep Learning
- Simple Feedforward Networks
- Computation Graphs for Deep Learning
- Convolutional Networks
- Learning Algorithms
- Generalization
- Recurrent Neural Networks
- Unsupervised Learning and Transfer Learning
- Applications
- Reinforcement Learning
- Learning from Rewards
- Passive Reinforcement Learning
- Active Reinforcement Learning
- Generalization in Reinforcement Learning
- Policy Search
- Apprenticeship and Inverse Reinforcement Leaming
- Applications of Reinforcement Learning
- Chapter VI Communicating, perceiving, and acting
- Natural Language Processing
- Language Models
- Grammar
- Parsing
- Augmented Grammars
- Complications of Real Natural Languagr
- Natural Language Tasks
- Deep Learning for Natural Language Processing
- Word Embeddings
- Recurrent Neural Networks for NLP
- Sequence-to-Sequence Models
- The Transformer Architecture
- Pretraining and Transfer Learning
- State of the art
- Robotics
- Robots
- Robot Hardware
- What kind of problem is robotics solving?
- Robotic Perception
- Planning and Control
- Planning Uncertain Movements
- Reinforcement Laming in Robotics
- Humans and Robots
- Alternative Robotic Frameworks
- Application Domains
- Computer Vision
- Introduction
- Image Formation
- Simple Image Features
- Classifying Images
- Detecting Objects
- The 3D World
- Using Computer Vision
- Chapter VII Conclusions
- Philosophy, Ethics, and Safety of Al
- The Limits of Al
- Can Machines Really Think?
- The Ethics of Al
- The Future of AI
- Al Components
- Al Architectures
- A.1 Complexity Analysis and O0 Notation
- A.2 Vectors, Matrices, and Linear Algebra
- A.3 Probability Distributions
- Bibliographical and Historical Notes
B Notes on Languages and Algorithms
- B.1 Defining Languages with Backus-Naur Form (BNF)
- B.2 Describing Algorithms with Pseudocode
- B.3 Online Supplemental Material
Bibliography Index
- In-depth coverage of both basic and advanced topics provides students with a basic understanding of the discipline without compromising complexity and depth.
- Non-technical learning material makes content more accessible, introducing major concepts before going into mathematical algorithmic details.
- Unified approach to AI showcases how the various sub-fields of AI fit together.
- Flexible format offers adaptable text for varying instructors' preferences.
- A comprehensive index and extensive bibliography support student learning, covering a wide range of topics relevant to the content.
- New chapters feature expanded coverage of probabilistic programming (Ch. 18); multi-agent decision making (Ch. 17 with Michael Wooldridge); deep learning (Ch.22 with Ian Goodfellow); and deep learning for natural language processing (Ch. 25 with Jacob Devlin and Mei-Wing Chang).
- Significantly updated material on robotics includes robots that interact with humans and the application of reinforcement learning to robotics.
- Revised coverage of computer vision, natural language understanding, and speech recognition reflects the impact of deep learning methods on these fields.
- The Author-Maintained Website at http://aima.cs.berkeley.edu/ includes text-related comments and discussions, exercises, an online code repository, Instructor Resources, and more.
- New sections include content on topics, such as causality by Judea Pearl, and the Monte Carlo search for games and robotics.
- New sections discuss transfer learning for deep learning in general and for natural language.
- New sections include content on privacy, fairness, future of work, and safe AI.
Produktdetaljer
Biographical note
Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California, Berkeley, where he is a Professor and former Chair of Computer Science, Director of the Centre for Human-Compatible AI, and holder of the Smith–Zadeh Chair in Engineering.
In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was co-winner of the Computers and Thought Award. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science, and Honorary Fellow of Wadham College, Oxford, and an Andrew Carnegie Fellow. He held the Chaire Blaise Pascal in Paris from 2012 to 2014. He has published over 300 papers on a wide range of topics in artificial intelligence. His other books include: The Use of Knowledge in Analogy and Induction, Do the Right Thing: Studies in Limited Rationality (with Eric Wefald), and Human Compatible: Artificial Intelligence and the Problem of Control.
Peter Norvig is currently Director of Research at Google, Inc., and was the director responsible for the core Web search algorithms from 2002 to 2005. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Previously, he was head of the Computational Sciences Division at NASA Ames Research Center, where he oversaw NASA's research and development in artificial intelligence and robotics, and chief scientist at Junglee, where he helped develop one of the first Internet information extraction services. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from the University of California at Berkeley.
He received the Distinguished Alumni and Engineering Innovation awards from Berkeley and the Exceptional Achievement Medal from NASA. He has been a professor at the University of Southern California and are research faculty member at Berkeley. His other books are: Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX.
The two authors shared the inaugural AAAI/EAAI Outstanding Educator award in 2016.