**Artificial Intelligence: A Modern Approach 4th Edition by Stuart Russell, ISBN-13: 978-0134610993**

[PDF eBook eTextbook] – **Available Instantly**

- Publisher: Pearson; 4th edition (April 28, 2020)
- Language: English
- 1136 pages
- ISBN-10: 0134610997
- ISBN-13: 978-0134610993

**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

*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.*

**4th Edition****Artificial Intelligence** is your guide to the theory and practice of modern AI. It introduces major concepts using intuitive explanations and nontechnical language, before going into mathematical or algorithmic details. In-depth coverage of both basic and advanced topics provides you with a solid understanding of the frontiers of AI without compromising complexity and depth. A unified approach to AI clearly details how the various subfields of AI fit together to build actual, useful programs.

The * 4th Edition* has been updated to stay current with the latest technologies as well as to present concepts in a more unified manner. New chapters feature expanded coverage of probabilistic programming, multiagent decision making, deep learning and deep learning for natural language processing. Revised coverage of computer vision, natural language understanding and speech recognition reflect the impact of deep learning methods on these fields.

**Table of Contents:**

Preface

New to this edition

Overview of the book

Online resources

Book cover

About the Authors

Contents

I Artificial Intelligence

Chapter 1 Introduction

1.1 What Is AI?

1.1.1 Acting humanly: The Turing test approach

1.1.2 Thinking humanly: The cognitive modeling approach

1.1.3 Thinking rationally: The “laws of thought” approach

1.1.4 Acting rationally: The rational agent approach

1.1.5 Beneficial machines

1.2 The Foundations of Artificial Intelligence

1.2.1 **Philosophy**

1.2.2 **Mathematics**

1.2.3 **Economics**

1.2.4 **Neuroscience**

1.2.5 **Psychology**

1.2.6 Computer engineering

1.2.7 Control theory and cybernetics

1.2.8 Linguistics

1.3 The History of Artificial Intelligence

1.3.1 The inception of artificial intelligence (1943–1956)

1.3.2 Early enthusiasm, great expectations (1952–1969)

1.3.3 A dose of reality (1966–1973)

1.3.4 Expert systems (1969–1986)

1.3.5 The return of neural networks (1986–present)

1.3.6 Probabilistic reasoning and machine learning (1987–present)

1.3.7 Big data (2001–present)

1.3.8 Deep learning (2011–present)

1.4 The State of the Art

1.5 Risks and Benefits of AI

Summary

Bibliographical and Historical Notes

Chapter 2 Intelligent Agents

2.1 Agents and Environments

2.2 Good Behavior: The Concept of Rationality

2.2.1 Performance measures

2.2.2 Rationality

2.2.3 Omniscience, learning, and autonomy

2.3 The Nature of Environments

2.3.1 Specifying the task environment

2.3.2 Properties of task environments

2.4 The Structure of Agents

2.4.1 Agent programs

2.4.2 Simple reflex agents

2.4.3 Model-based reflex agents

2.4.4 Goal-based agents

2.4.5 Utility-based agents

2.4.6 Learning agents

2.4.7 How the components of agent programs work

Summary

Bibliographical and Historical Notes

II Problem-solving

Chapter 3 Solving Problems by Searching

3.1 Problem-Solving Agents

3.1.1 Search problems and solutions

3.1.2 Formulating problems

3.2 Example Problems

3.2.1 Standardized problems

3.2.2 Real-world problems

3.3 Search Algorithms

3.3.1 Best-first search

3.3.2 Search data structures

3.3.3 Redundant paths

3.3.4 Measuring problem-solving performance

3.4 Uninformed Search Strategies

3.4.1 Breadth-first search

3.4.2 Dijkstra’s algorithm or uniform-cost search

3.4.3 Depth-first search and the problem of memory

3.4.4 Depth-limited and iterative deepening search

3.4.5 Bidirectional search

3.4.6 Comparing uninformed search algorithms

3.5 Informed (Heuristic) Search Strategies

3.5.1 Greedy best-first search

3.5.2 A* search

3.5.3 Search contours

3.5.4 Satisficing search: Inadmissible heuristics and weighted A*

3.5.5 Memory-bounded search

3.5.6 Bidirectional heuristic search

3.6 Heuristic Functions

3.6.1 The effect of heuristic accuracy on performance

3.6.2 Generating heuristics from relaxed problems

3.6.3 Generating heuristics from subproblems: Pattern databases

3.6.4 Generating heuristics with landmarks

3.6.5 Learning to search better

3.6.6 Learning heuristics from experience

Summary

Bibliographical and Historical Notes

Chapter 4 Search in Complex Environments

4.1 Local Search and Optimization Problems

4.1.1 Hill-climbing search

4.1.2 Simulated annealing

4.1.3 Local beam search

4.1.4 Evolutionary algorithms

4.2 Local Search in Continuous Spaces

4.3 Search with Nondeterministic Actions

4.3.1 The erratic vacuum world

4.3.2 and–or search trees

4.3.3 Try, try again

4.4 Search in Partially Observable Environments

4.4.1 Searching with no observation

4.4.2 Searching in partially observable environments

4.4.3 Solving partially observable problems

4.4.4 An agent for partially observable environments

4.5 Online Search Agents and Unknown Environments

4.5.1 Online search problems

4.5.2 Online search agents

4.5.3 Online local search

4.5.4 Learning in online search

Summary

Bibliographical and Historical Notes

Chapter 5 Adversarial Search and Games

5.1 Game Theory

5.1.1 Two-player zero-sum games

5.2 Optimal Decisions in Games

5.2.1 The minimax search algorithm

5.2.2 Optimal decisions in multiplayer games

5.2.3 Alpha–Beta Pruning

5.2.4 Move ordering

5.3 Heuristic Alpha–Beta Tree Search

5.3.1 Evaluation functions

5.3.2 Cutting off search

5.3.3 Forward pruning

5.3.4 Search versus lookup

5.4 Monte Carlo Tree Search

5.5 Stochastic Games

5.5.1 Evaluation functions for games of chance

5.6 Partially Observable Games

5.6.1 Kriegspiel: Partially observable chess

5.6.2 Card games

5.7 Limitations of Game Search Algorithms

Summary

Bibliographical and Historical Notes

Chapter 6 Constraint Satisfaction Problems

6.1 Defining Constraint Satisfaction Problems

6.1.1 Example problem: Map coloring

6.1.2 Example problem: Job-shop scheduling

6.1.3 Variations on the CSP formalism

6.2 Constraint Propagation: Inference in CSPs

6.2.1 Node consistency

6.2.2 Arc consistency

6.2.3 Path consistency

6.2.4 K-consistency

6.2.5 Global constraints

6.2.6 Sudoku

6.3 Backtracking Search for CSPs

6.3.1 Variable and value ordering

6.3.2 Interleaving search and inference

6.3.3 Intelligent backtracking: Looking backward

6.3.4 Constraint learning

6.4 Local Search for CSPs

6.5 The Structure of Problems

6.5.1 Cutset conditioning

6.5.2 Tree decomposition

6.5.3 Value symmetry

Summary

Bibliographical and Historical Notes

III Knowledge, reasoning, and planning

Chapter 7 Logical Agents

7.1 Knowledge-Based Agents

7.2 The Wumpus World

7.3 Logic

7.4 Propositional Logic: A Very Simple Logic

7.4.1 Syntax

7.4.2 Semantics

7.4.3 A simple knowledge base

7.4.4 A simple inference procedure

7.5 Propositional Theorem Proving

7.5.1 Inference and proofs

7.5.2 Proof by resolution

Conjunctive normal form

A resolution algorithm

Completeness of resolution

7.5.3 Horn clauses and definite clauses

7.5.4 Forward and backward chaining

7.6 Effective Propositional Model Checking

7.6.1 A complete backtracking algorithm

7.6.2 Local search algorithms

7.6.3 The landscape of random SAT problems

7.7 Agents Based on Propositional Logic

7.7.1 The current state of the world

7.7.2 A hybrid agent

7.7.3 Logical state estimation

7.7.4 Making plans by propositional inference

Summary

Bibliographical and Historical Notes

Chapter 8 First-Order Logic

8.1 Representation Revisited

8.1.1 The language of thought

8.1.2 Combining the best of formal and natural languages

8.2 Syntax and Semantics of First-Order Logic

8.2.1 Models for first-order logic

8.2.2 Symbols and interpretations

8.2.3 Terms

8.2.4 Atomic sentences

8.2.5 Complex sentences

8.2.6 Quantifiers

Universal quantification (∀)

Existential quantification (∃)

Nested quantifiers

Connections between ∀ and ∃

8.2.7 Equality

8.2.8 Database semantics

8.3 Using First-Order Logic

8.3.1 Assertions and queries in first-order logic

8.3.2 The kinship domain

8.3.3 Numbers, sets, and lists

8.3.4 The wumpus world

8.4 Knowledge Engineering in First-Order Logic

8.4.1 The knowledge engineering process

8.4.2 The electronic circuits domain

Identify the questions

Assemble the relevant knowledge

Decide on a vocabulary

Encode general knowledge of the domain

Encode the specific problem instance

Pose queries to the inference procedure

Debug the knowledge base

Summary

Bibliographical and Historical Notes

Chapter 9 Inference in First-Order Logic

9.1 Propositional vs. First-Order Inference

9.1.1 Reduction to propositional inference

9.2 Unification and First-Order Inference

9.2.1 Unification

9.2.2 Storage and retrieval

9.3 Forward Chaining

9.3.1 First-order definite clauses

9.3.2 A simple forward-chaining algorithm

9.3.3 Efficient forward chaining

Matching rules against known facts

Incremental forward chaining

Irrelevant facts

9.4 Backward Chaining

9.4.1 A backward-chaining algorithm

9.4.2 Logic programming

9.4.3 Redundant inference and infinite loops

9.4.4 Database semantics of Prolog

9.4.5 Constraint logic programming

9.5 Resolution

9.5.1 Conjunctive normal form for first-order logic

9.5.2 The resolution inference rule

9.5.3 Example proofs

9.5.4 Completeness of resolution

9.5.5 Equality

9.5.6 Resolution strategies

Practical uses of resolution theorem provers

Summary

Bibliographical and Historical Notes

Chapter 10 Knowledge Representation

10.1 Ontological Engineering

10.2 Categories and Objects

10.2.1 Physical composition

10.2.2 Measurements

10.2.3 Objects: Things and stuff

10.3 Events

10.3.1 Time

10.3.2 Fluents and objects

10.4 Mental Objects and Modal Logic

10.4.1 Other modal logics

10.5 Reasoning Systems for Categories

10.5.1 Semantic networks

10.5.2 Description logics

10.6 Reasoning with Default Information

10.6.1 Circumscription and default logic

10.6.2 Truth maintenance systems

Summary

Bibliographical and Historical Notes

Chapter 11 Automated Planning

11.1 Definition of Classical Planning

11.1.1 Example domain: Air cargo transport

11.1.2 Example domain: The spare tire problem

11.1.3 Example domain: The blocks world

11.2 Algorithms for Classical Planning

11.2.1 Forward state-space search for planning

11.2.2 Backward search for planning

11.2.3 Planning as Boolean satisfiability

11.2.4 Other classical planning approaches

11.3 Heuristics for Planning

11.3.1 Domain-independent pruning

11.3.2 State abstraction in planning

11.4 Hierarchical Planning

11.4.1 High-level actions

11.4.2 Searching for primitive solutions

11.4.3 Searching for abstract solutions

11.5 Planning and Acting in Nondeterministic Domains

11.5.1 Sensorless planning

11.5.2 Contingent planning

11.5.3 Online planning

11.6 Time, Schedules, and Resources

11.6.1 Representing temporal and resource constraints

11.6.2 Solving scheduling problems

11.7 Analysis of Planning Approaches

Summary

Bibliographical and Historical Notes

IV Uncertain knowledge and reasoning

Chapter 12 Quantifying Uncertainty

12.1 Acting under Uncertainty

12.1.1 Summarizing uncertainty

12.1.2 Uncertainty and rational decisions

12.2 Basic Probability Notation

12.2.1 What probabilities are about

12.2.2 The language of propositions in probability assertions

12.2.3 Probability axioms and their reasonableness

12.3 Inference Using Full Joint Distributions

12.4 Independence

12.5 Bayes’ Rule and Its Use

12.5.1 Applying Bayes’ rule: The simple case

12.5.2 Using Bayes’ rule: Combining evidence

12.6 Naive Bayes Models

12.6.1 Text classification with naive Bayes

12.7 The Wumpus World Revisited

Summary

Bibliographical and Historical Notes

Chapter 13 Probabilistic Reasoning

13.1 Representing Knowledge in an Uncertain Domain

13.2 The Semantics of Bayesian Networks

13.2.1 Conditional independence relations in Bayesian networks

13.2.2 Efficient Representation of Conditional Distributions

13.2.3 Bayesian nets with continuous variables

13.2.4 Case study: Car insurance

13.3 Exact Inference in Bayesian Networks

13.3.1 Inference by enumeration

13.3.2 The variable elimination algorithm

Operations on factors

Variable ordering and variable relevance

13.3.3 The complexity of exact inference

13.3.4 Clustering algorithms

13.4 Approximate Inference for Bayesian Networks

13.4.1 Direct sampling methods

Rejection sampling in Bayesian networks

Importance sampling

13.4.2 Inference by Markov chain simulation

Gibbs sampling in Bayesian networks

Analysis of Markov chains

Why Gibbs sampling works

Complexity of Gibbs sampling

Metropolis–Hastings sampling

13.4.3 Compiling approximate inference

13.5 Causal Networks

13.5.1 Representing actions: The do-operator

13.5.2 The back-door criterion

Summary

Bibliographical and Historical Notes

Chapter 14 Probabilistic Reasoning over Time

14.1 Time and Uncertainty

14.1.1 States and observations

14.1.2 Transition and sensor models

14.2 Inference in Temporal Models

14.2.1 Filtering and prediction

14.2.2 Smoothing

14.2.3 Finding the most likely sequence

14.3 Hidden Markov Models

14.3.1 Simplified matrix algorithms

14.3.2 Hidden Markov model example: Localization

14.4 Kalman Filters

14.4.1 Updating Gaussian distributions

14.4.2 A simple one-dimensional example

14.4.3 The general case

14.4.4 Applicability of Kalman filtering

14.5 Dynamic Bayesian Networks

14.5.1 Constructing DBNs

14.5.2 Exact inference in DBNs

14.5.3 Approximate inference in DBNs

Summary

Bibliographical and Historical Notes

Chapter 15 Probabilistic Programming

15.1 Relational Probability Models

15.1.1 Syntax and semantics

15.1.2 Example: Rating player skill levels

15.1.3 Inference in relational probability models

15.2 Open-Universe Probability Models

15.2.1 Syntax and semantics

15.2.2 Inference in open-universe probability models

15.2.3 Examples

Citation matching

Nuclear treaty monitoring

15.3 Keeping Track of a Complex World

15.3.1 Example: Multitarget tracking

15.3.2 Example: Traffic monitoring

15.4 Programs as Probability Models

15.4.1 Example: Reading text

15.4.2 Syntax and semantics

15.4.3 Inference results

15.4.4 Improving the generative program to incorporate a Markov model

15.4.5 Inference in generative programs

Summary

Bibliographical and Historical Notes

Chapter 16 Making Simple Decisions

16.1 Combining Beliefs and Desires under Uncertainty

16.2 The Basis of Utility Theory

16.2.1 Constraints on rational preferences

16.2.2 Rational preferences lead to utility

16.3 Utility Functions

16.3.1 Utility assessment and utility scales

16.3.2 The utility of money

16.3.3 Expected utility and post-decision disappointment

16.3.4 Human judgment and irrationality

16.4 Multiattribute Utility Functions

16.4.1 Dominance

16.4.2 Preference structure and multiattribute utility

Preferences without uncertainty

Preferences with uncertainty

16.5 Decision Networks

16.5.1 Representing a decision problem with a decision network

16.5.2 Evaluating decision networks

16.6 The Value of Information

16.6.1 A simple example

16.6.2 A general formula for perfect information

16.6.3 Properties of the value of information

16.6.4 Implementation of an information-gathering agent

16.6.5 Nonmyopic information gathering

16.6.6 Sensitivity analysis and robust decisions

16.7 Unknown Preferences

16.7.1 Uncertainty about one’s own preferences

16.7.2 Deference to humans

Summary

Bibliographical and Historical Notes

Chapter 17 Making Complex Decisions

17.1 Sequential Decision Problems

17.1.1 Utilities over time

17.1.2 Optimal policies and the utilities of states

17.1.3 Reward scales

17.1.4 Representing MDPs

17.2 Algorithms for MDPs

17.2.1 Value Iteration

Convergence of value iteration

17.2.2 Policy iteration

17.2.3 Linear programming

17.2.4 Online algorithms for MDPs

17.3 Bandit Problems

17.3.1 Calculating the Gittins index

17.3.2 The Bernoulli bandit

17.3.3 Approximately optimal bandit policies

17.3.4 Non-indexable variants

17.4 Partially Observable MDPs

17.4.1 Definition of POMDPs

17.5 Algorithms for Solving POMDPs

17.5.1 Value iteration for POMDPs

17.5.2 Online algorithms for POMDPs

Summary

Bibliographical and Historical Notes

Chapter 18 Multiagent Decision Making

18.1 Properties of Multiagent Environments

18.1.1 One decision maker

18.1.2 Multiple decision makers

18.1.3 Multiagent planning

18.1.4 Planning with multiple agents: Cooperation and coordination

18.2 Non-Cooperative Game Theory

18.2.1 Games with a single move: Normal form games

18.2.2 Social welfare

**Computing** equilibria

18.2.3 Repeated games

18.2.4 Sequential games: The extensive form

Chance and simultaneous moves

Capturing imperfect information

18.2.5 Uncertain payoffs and assistance games

18.3 Cooperative Game Theory

18.3.1 Coalition structures and outcomes

18.3.2 Strategy in cooperative games

18.3.3 Computation in cooperative games

Marginal contribution nets

Coalition structures for maximum social welfare

18.4 Making Collective Decisions

18.4.1 Allocating tasks with the contract net

18.4.2 Allocating scarce resources with auctions

Common goods

18.4.3 Voting

Strategic manipulation

18.4.4 Bargaining

Bargaining with the alternating offers protocol

Impatient agents

Negotiation in task-oriented domains

The monotonic concession protocol

The Zeuthen strategy

Summary

Bibliographical and Historical Notes

V Machine Learning

Chapter 19 Learning from Examples

19.1 Forms of Learning

19.2 Supervised Learning

19.2.1 Example problem: Restaurant waiting

19.3 Learning Decision Trees

19.3.1 Expressiveness of decision trees

19.3.2 Learning decision trees from examples

19.3.3 Choosing attribute tests

19.3.4 Generalization and overfitting

19.3.5 Broadening the applicability of decision trees

19.4 Model Selection and Optimization

19.4.1 Model selection

19.4.2 From error rates to loss

19.4.3 Regularization

19.4.4 Hyperparameter tuning

19.5 The Theory of Learning

19.5.1 PAC learning example: Learning decision lists

19.6 Linear Regression and Classification

19.6.1 Univariate linear regression

19.6.2 Gradient descent

19.6.3 Multivariable linear regression

19.6.4 Linear classifiers with a hard threshold

19.6.5 Linear classification with logistic regression

19.7 Nonparametric Models

19.7.1 Nearest-neighbor models

19.7.2 Finding nearest neighbors with k-d trees

19.7.3 Locality-sensitive hashing

19.7.4 Nonparametric regression

19.7.5 Support vector machines

19.7.6 The kernel trick

19.8 Ensemble Learning

19.8.1 Bagging

19.8.2 Random forests

19.8.3 Stacking

19.8.4 Boosting

19.8.5 Gradient boosting

19.8.6 Online learning

19.9 Developing Machine Learning Systems

19.9.1 Problem formulation

19.9.2 Data collection, assessment, and management

Feature engineering

Exploratory data analysis and visualization

19.9.3 Model selection and training

19.9.4 Trust, interpretability, and explainability

19.9.5 Operation, monitoring, and maintenance

Summary

Bibliographical and Historical Notes

Chapter 20 Learning Probabilistic Models

20.1 Statistical Learning

20.2 Learning with Complete Data

20.2.1 Maximum-likelihood parameter learning: Discrete models

20.2.2 Naive Bayes models

20.2.3 Generative and discriminative models

20.2.4 Maximum-likelihood parameter learning: Continuous models

20.2.5 Bayesian parameter learning

20.2.6 Bayesian linear regression

20.2.7 Learning Bayes net structures

20.2.8 Density estimation with nonparametric models

20.3 Learning with Hidden Variables: The EM Algorithm

20.3.1 Unsupervised clustering: Learning mixtures of Gaussians

20.3.2 Learning Bayes net parameter values for hidden variables

20.3.3 Learning hidden Markov models

20.3.4 The general form of the EM algorithm

20.3.5 Learning Bayes net structures with hidden variables

Summary

Bibliographical and Historical Notes

Chapter 21 Deep Learning

21.1 Simple Feedforward Networks

21.1.1 Networks as complex functions

21.1.2 Gradients and learning

21.2 Computation Graphs for Deep Learning

21.2.1 Input encoding

21.2.2 Output layers and loss functions

21.2.3 Hidden layers

21.3 Convolutional Networks

21.3.1 Pooling and downsampling

21.3.2 Tensor operations in CNNs

21.3.3 Residual networks

21.4 Learning Algorithms

21.4.1 Computing gradients in computation graphs

21.4.2 Batch normalization

21.5 Generalization

21.5.1 Choosing a network architecture

21.5.2 Neural architecture search

21.5.3 Weight decay

21.5.4 Dropout

21.6 Recurrent Neural Networks

21.6.1 Training a basic RNN

21.6.2 Long short-term memory RNNs

21.7 Unsupervised Learning and Transfer Learning

21.7.1 Unsupervised learning

Probabilistic PCA: A simple generative model

Autoencoders

Deep autoregressive models

Generative adversarial networks

Unsupervised translation

21.7.2 Transfer learning and multitask learning

21.8 Applications

21.8.1 Vision

21.8.2 Natural language processing

21.8.3 Reinforcement learning

Summary

Bibliographical and Historical Notes

Chapter 22 Reinforcement Learning

22.1 Learning from Rewards

22.2 Passive Reinforcement Learning

22.2.1 Direct utility estimation

22.2.2 Adaptive dynamic programming

22.2.3 Temporal-difference learning

22.3 Active Reinforcement Learning

22.3.1 Exploration

22.3.2 Safe exploration

22.3.3 Temporal-difference Q-learning

22.4 Generalization in Reinforcement Learning

22.4.1 Approximating direct utility estimation

22.4.2 Approximating temporal-difference learning

22.4.3 Deep reinforcement learning

22.4.4 Reward shaping

22.4.5 Hierarchical reinforcement learning

22.5 Policy Search

22.6 Apprenticeship and Inverse Reinforcement Learning

22.7 Applications of Reinforcement Learning

22.7.1 Applications in game playing

22.7.2 Application to robot control

Summary

Bibliographical and Historical Notes

VI Communicating, perceiving, and acting

Chapter 23 Natural Language Processing

23.1 Language Models

23.1.1 The bag-of-words model

23.1.2 N-gram word models

23.1.3 Other n-gram models

23.1.4 Smoothing n-gram models

23.1.5 Word representations

23.1.6 Part-of-speech (POS) tagging

23.1.7 Comparing language models

23.2 Grammar

23.2.1 The lexicon of E0

23.3 Parsing

23.3.1 Dependency parsing

23.3.2 Learning a parser from examples

23.4 Augmented Grammars

23.4.1 Semantic interpretation

23.4.2 Learning semantic grammars

23.5 Complications of Real Natural Language

23.6 Natural Language Tasks

Summary

Bibliographical and Historical Notes

Chapter 24 Deep Learning for Natural Language Processing

24.1 Word Embeddings

24.2 Recurrent Neural Networks for NLP

24.2.1 Language models with recurrent neural networks

24.2.2 Classification with recurrent neural networks

24.2.3 LSTMs for NLP tasks

24.3 Sequence-to-Sequence Models

24.3.1 Attention

24.3.2 Decoding

24.4 The Transformer Architecture

24.4.1 Self-attention

24.4.2 From self-attention to transformer

24.5 Pretraining and Transfer Learning

24.5.1 Pretrained word embeddings

24.5.2 Pretrained contextual representations

24.5.3 Masked language models

24.6 State of the art

Summary

Bibliographical and Historical Notes

Chapter 25 Computer Vision

25.1 Introduction

25.2 Image Formation

25.2.1 Images without lenses: The pinhole camera

25.2.2 Lens systems

25.2.3 Scaled orthographic projection

25.2.4 Light and shading

25.2.5 Color

25.3 Simple Image Features

25.3.1 Edges

25.3.2 Texture

25.3.3 Optical flow

25.3.4 Segmentation of natural images

25.4 Classifying Images

25.4.1 Image classification with convolutional neural networks

25.4.2 Why convolutional neural networks classify images well

25.5 Detecting Objects

25.6 The 3D World

25.6.1 3D cues from multiple views

25.6.2 Binocular stereopsis

25.6.3 3D cues from a moving camera

25.6.4 3D cues from one view

25.7 Using Computer Vision

25.7.1 Understanding what people are doing

25.7.2 Linking pictures and words

25.7.3 Reconstruction from many views

25.7.4 Geometry from a single view

25.7.5 Making pictures

25.7.6 Controlling movement with vision

Summary

Bibliographical and Historical Notes

Chapter 26 Robotics

26.1 Robots

26.2 Robot Hardware

26.2 Types of robots from the hardware perspective

26.2.2 Sensing the world

26.2.3 Producing motion

26.3 What kind of problem is robotics solving?

26.4 Robotic Perception

26.4.1 Localization and mapping

26.4.2 Other types of perception

26.4.3 Supervised and unsupervised learning in robot perception

26.5 Planning and Control

26.5.1 Configuration space

26.5.2 Motion planning

Visibility graphs

Voronoi diagrams

Cell decomposition

Randomized motion planning

Rapidly-exploring random trees

Trajectory optimization for kinematic planning

26.5.3 Trajectory tracking control

Plans versus policies

26.5.4 Optimal control

26.6 Planning Uncertain Movements

26.7 Reinforcement Learning in Robotics

26.7.1 Exploiting models

26.7.2 Exploiting other information

26.8 Humans and Robots

26.8.1 Coordination

Humans as approximately rational agents

Predicting human action

Human predictions about the robot

Humans as black box agents

26.8.2 Learning to do what humans want

Preference learning: Learning cost functions

Learning policies directly via imitation

26.9 Alternative Robotic Frameworks

26.9.1 Reactive controllers

26.9.2 Subsumption architectures

26.10 Application Domains

Summary

Bibliographical and Historical Notes

VII Conclusions

Chapter 27 Philosophy, Ethics, and Safety of AI

27.1 The Limits of AI

27.1.1 The argument from informality

27.1.2 The argument from disability

27.1.3 The mathematical objection

27.1.4 Measuring AI

27.2 Can Machines Really Think?

27.2.1 The Chinese room

27.2.2 Consciousness and qualia

27.3 The Ethics of AI

27.3.1 Lethal autonomous weapons

27.3.2 Surveillance, security, and privacy

27.3.3 Fairness and bias

27.3.4 Trust and transparency

27.3.5 The future of work

27.3.6 Robot rights

27.3.7 AI Safety

Summary

Bibliographical and Historical Notes

Chapter 28 The Future of AI

28.1 AI Components

28.2 AI Architectures

Appendix A Mathematical Background

A.1 Complexity Analysis and O() Notation

A.1.1 Asymptotic analysis

A.1.2 NP and inherently hard problems

A.2 Vectors, Matrices, and Linear Algebra

A.3 Probability Distributions

Bibliographical and Historical Notes

Appendix 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

Symbols

* 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

*, where he is a professor and former chair of computer science, director of the Center 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.*

**University of California at Berkeley*** 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

*and a 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.*

**University of Southern California**The two authors shared the inaugural AAAI/EAAI Outstanding Educator award in 2016.

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