Description
Book SynopsisStuart 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 SmithZadeh 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 a
Table of Contents
Chapter I Artificial Intelligence
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Introduction
- What Is AI?
- The Foundations of Artificial Intelligence
- The History of Artificial Intelligence
- The State of the Art
- Risks and Benefits of AI
SummaryBibliographical and Historical Notes
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Intelligent Agents
- Agents and Environments
- Good Behavior: The Concept of Rationality
- The Nature of Environments
- The Structure of Agents
SummaryBibliographical and Historical Notes
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Chapter II Problem Solving
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Solving Problems by Searching
- Problem-Solving Agents
- Example Problems
- Search Algorithms
- Uninformed Search Strategies
- Informed (Heuristic) Search Strategies
- Heuristic Functions
SummaryBibliographical and Historical Notes
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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
SummaryBibliographical and Historical Notes
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Constraint Satisfaction Problems
- Defining Constraint Satisfaction Problems
- Constraint Propagation: Inference in CSPs
- Backtracking Search for CSPs
- Local Search for CSPs
- The Structure of Problems
SummaryBibliographical and Historical Notes
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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
SummaryBibliographical and Historical Notes
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Chapter III Knowledge, Reasoning and Planning
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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
SummaryBibliographical and Historical Notes
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First-Order Logic
- Representation Revisited
- Syntax and Semantics of First-Order Logic
- Using First-Order Logic
- Knowledge Engineering in First-Order Logic
SummaryBibliographical and Historical Notes
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Inference in First-Order Logic
- Propositional vs. First-Order Inference
- Unification and First-Order Inference
- Forward Chaining
- Backward Chaining
- Resolution
SummaryBibliographical and Historical Notes
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Knowledge Representation
- Ontological Engineering
- Categories and Objects
- Events
- Mental Objects and Modal Logic
- for Categories
- Reasoning with Default Information
SummaryBibliographical and Historical Notes
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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
SummaryBibliographical and Historical Notes
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Chapter IV Uncertain Knowledge and Reasoning
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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
SummaryBibliographical and Historical Notes
-
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
SummaryBibliographical and Historical Notes
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Probabilistic Reasoning over Time
- Time and Uncertainty
- Inference in Temporal Models
- Hidden Markov Models
- Kalman Filters
- Dynamic Bayesian Networks
SummaryBibliographical and Historical Notes
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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
SummaryBibliographical and Historical Notes
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Making Complex Decisions
- Sequential Decision Problems
- Algorithms for MDPs
- Bandit Problems
- Partially Observable MDPs
- Algorithms for Solving POMDPs
SummaryBibliographical and Historical Notes
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Multiagent Decision Making
- Properties of Multiagent Environments
- Non-Cooperative Game Theory
- Cooperative Game Theory
- Making Collective Decisions
SummaryBibliographical and Historical Notes
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Probabilistic Programming
- Relational Probability Models
- Open-Universe Probability Models
- Keeping Track of a Complex World
- Programs as Probability Models
SummaryBibliographical and Historical Notes
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Chapter V Machine Learning
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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
SummaryBibliographical and Historical Notes
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Knowledge in Learning
- A Logical Formulation of Learning
- Knowledge in Learning
- Exmplanation-Based Leaening
- Learning Using Relevance Information
- Inductive Logic Programming
SummaryBibliographical and Historical Notes
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Learning Probabilistic Models
- Statistical Learning
- Learning with Complete Data
- Learning with Hidden Variables: The EM Algorithm
SummaryBibliographical and Historical Notes
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Deep Learning
- Simple Feedforward Networks
- Computation Graphs for Deep Learning
- Convolutional Networks
- Learning Algorithms
- Generalization
- Recurrent Neural Networks
- Unsupervised Learning and Transfer Learning
- Applications
SummaryBibliographical and Historical Notes
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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
SummaryBibliographical and Historical Notes
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Chapter VI Communicating, perceiving, and acting
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Natural Language Processing
- Language Models
- Grammar
- Parsing
- Augmented Grammars
- Complications of Real Natural Languagr
- Natural Language Tasks
SummaryBibliographical and Historical Notes
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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
SummaryBibliographical and Historical Notes
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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
SummaryBibliographical and Historical Notes
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Computer Vision
- Introduction
- Image Formation
- Simple Image Features
- Classifying Images
- Detecting Objects
- The 3D World
- Using Computer Vision
SummaryBibliographical and Historical Notes
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Chapter VII Conclusions
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Philosophy, Ethics, and Safety of Al
- The Limits of Al
- Can Machines Really Think?
- The Ethics of Al
SummaryBibliographical and Historical Notes
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The Future of AI
- Al Components
- Al Architectures
A Mathematical Background
- 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
