Artificial Intelligence: A Modern Approach, Fourth Global Edition [4th Ed] (Instructor Res. n. 1 of 2, Solution Manual, Solutions)

Cover
Half Title
AI Pearson Series in Artificial Intelligence
Title Page
Copyright
Dedication
Preface
About the Authors
Contents
I: Artificial Intelligence
	Chapter 1: Introduction
		1.1 What Is AI?
		1.2 The Foundations of Artificial Intelligence
		1.3 The History of Artificial Intelligence
		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
		2.3 The Nature of Environments
		2.4 The Structure of Agents
		Summary
		Bibliographical and Historical Notes
II: Problem-solving
	Chapter 3: Solving Problems by Searching
		3.1 Problem-Solving Agents
		3.2 Example Problems
		3.3 Search Algorithms
		3.4 Uninformed Search Strategies
		3.5 Informed (Heuristic) Search Strategies
		3.6 Heuristic Functions
		Summary
		Bibliographical and Historical Notes
	Chapter 4: Search in Complex Environments
		4.1 Local Search and Optimization Problems
		4.2 Local Search in Continuous Spaces
		4.3 Search with Nondeterministic Actions
		4.4 Search in Partially Observable Environments
		4.5 Online Search Agents and Unknown Environments
		Summary
		Bibliographical and Historical Notes
	Chapter 5: Constraint Satisfaction Problems
		5.1 Defining Constraint Satisfaction Problems
		5.2 Constraint Propagation: Inference in CSPs
		5.3 Backtracking Search for CSPs
		5.4 Local Search for CSPs
		5.5 The Structure of Problems
		Summary
		Bibliographical and Historical Notes
	Chapter 6: Adversarial Search and Games
		6.1 Game Theory
		6.2 Optimal Decisions in Games
		6.3 Heuristic Alpha–Beta Tree Search
		6.4 Monte Carlo Tree Search
		6.5 Stochastic Games
		6.6 Partially Observable Games
		6.7 Limitations of Game Search Algorithms
		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
		7.5 Propositional Theorem Proving
		7.6 Effective Propositional Model Checking
		7.7 Agents Based on Propositional Logic
		Summary
		Bibliographical and Historical Notes
	Chapter 8: First-Order Logic
		8.1 Representation Revisited
		8.2 Syntax and Semantics of First-Order Logic
		8.3 Using First-Order Logic
		8.4 Knowledge Engineering in First-Order Logic
		Summary
		Bibliographical and Historical Notes
	Chapter 9: Inference in First-Order Logic
		9.1 Propositional vs. First-Order Inference
		9.2 Unification and First-Order Inference
		9.3 Forward Chaining
		9.4 Backward Chaining
		9.5 Resolution
		Summary
		Bibliographical and Historical Notes
	Chapter 10: Knowledge Representation
		10.1 Ontological Engineering
		10.2 Categories and Objects
		10.3 Events
		10.4 Mental Objects and Modal Logic
		10.5 Reasoning Systems for Categories
		10.6 Reasoning with Default Information
		Summary
		Bibliographical and Historical Notes
	Chapter 11: Automated Planning
		11.1 Definition of Classical Planning
		11.2 Algorithms for Classical Planning
		11.3 Heuristics for Planning
		11.4 Hierarchical Planning
		11.5 Planning and Acting in Nondeterministic Domains
		11.6 Time, Schedules, and Resources
		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
		12.2 Basic Probability Notation
		12.3 Inference Using Full Joint Distributions
		12.4 Independence
		12.5 Bayes’ Rule and Its Use
		12.6 Naive Bayes Models
		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
		13.3 Exact Inference in Bayesian Networks
		13.4 Approximate Inference for Bayesian Networks
		13.5 Causal Networks
		Summary
		Bibliographical and Historical Notes
	Chapter 14: Probabilistic Reasoning over Time
		14.1 Time and Uncertainty
		14.2 Inference in Temporal Models
		14.3 Hidden Markov Models
		14.4 Kalman Filters
		14.5 Dynamic Bayesian Networks
		Summary
		Bibliographical and Historical Notes
	Chapter 15: Making Simple Decisions
		15.1 Combining Beliefs and Desires under Uncertainty
		15.2 The Basis of Utility Theory
		15.3 Utility Functions
		15.4 Multiattribute Utility Functions
		15.5 Decision Networks
		15.6 The Value of Information
		15.7 Unknown Preferences
		Summary
		Bibliographical and Historical Notes
	Chapter 16: Making Complex Decisions
		16.1 Sequential Decision Problems
		16.2 Algorithms for MDPs
		16.3 Bandit Problems
		16.4 Partially Observable MDPs
		16.5 Algorithms for Solving POMDPs
		Summary
		Bibliographical and Historical Notes
	Chapter 17: Multiagent Decision Making
		17.1 Properties of Multiagent Environments
		17.2 Non-Cooperative Game Theory
		17.3 Cooperative Game Theory
		17.4 Making Collective Decisions
		Summary
		Bibliographical and Historical Notes
	Chapter 18: Probabilistic Programming
		18.1 Relational Probability Models
		18.2 Open-Universe Probability Models
		18.3 Keeping Track of a Complex World
		18.4 Programs as Probability Models
		Summary
		Bibliographical and Historical Notes
V: Machine Learning
	Chapter 19: Learning from Examples
		19.1 Forms of Learning
		19.2 Supervised Learning
		19.3 Learning Decision Trees
		19.4 Model Selection and Optimization
		19.5 The Theory of Learning
		19.6 Linear Regression and Classification
		19.7 Nonparametric Models
		19.8 Ensemble Learning
		19.9 Developing Machine Learning Systems
		Summary
		Bibliographical and Historical Notes
	Chapter 20: Knowledge in Learning
		20.1 A Logical Formulation of Learning
		20.2 Knowledge in Learning
		20.3 Explanation-Based Learning
		20.4 Learning Using Relevance Information
		20.5 Inductive Logic Programming
		Summary
		Bibliographical and Historical Notes
	Chapter 21: Learning Probabilistic Models
		21.1 Statistical Learning
		21.2 Learning with Complete Data
		21.3 Learning with Hidden Variables: The EM Algorithm
		Summary
		Bibliographical and Historical Notes
	Chapter 22: Deep Learning
		22.1 Simple Feedforward Networks
		22.2 Computation Graphs for Deep Learning
		22.3 Convolutional Networks
		22.4 Learning Algorithms
		22.5 Generalization
		22.6 Recurrent Neural Networks
		22.7 Unsupervised Learning and Transfer Learning
		22.8 Applications
		Summary
		Bibliographical and Historical Notes
	Chapter 23: Reinforcement Learning
		23.1 Learning from Rewards
		23.2 Passive Reinforcement Learning
		23.3 Active Reinforcement Learning
		23.4 Generalization in Reinforcement Learning
		23.5 Policy Search
		23.6 Apprenticeship and Inverse Reinforcement Learning
		23.7 Applications of Reinforcement Learning
		Summary
		Bibliographical and Historical Notes
VI: Communicating, perceiving, and acting
	Chapter 24: Natural Language Processing
		24.1 Language Models
		24.2 Grammar
		24.3 Parsing
		24.4 Augmented Grammars
		24.5 Complications of Real Natural Language
		24.6 Natural Language Tasks
		Summary
		Bibliographical and Historical Notes
	Chapter 25: Deep Learning for Natural Language Processing
		25.1 Word Embeddings
		25.2 Recurrent Neural Networks for NLP
		25.3 Sequence-to-Sequence Models
		25.4 The Transformer Architecture
		25.5 Pretraining and Transfer Learning
		25.6 State of the art
		Summary
		Bibliographical and Historical Notes
	Chapter 26: Robotics
		26.1 Robots
		26.2 Robot Hardware
		26.3 What kind of problem is robotics solving?
		26.4 Robotic Perception
		26.5 Planning and Control
		26.6 Planning Uncertain Movements
		26.7 Reinforcement Learning in Robotics
		26.8 Humans and Robots
		26.9 Alternative Robotic Frameworks
		26.10 Application Domains
		Summary
		Bibliographical and Historical Notes
	Chapter 27: Computer Vision
		27.1 Introduction
		27.2 Image Formation
		27.3 Simple Image Features
		27.4 Classifying Images
		27.5 Detecting Objects
		27.6 The 3D World
		27.7 Using Computer Vision
		Summary
		Bibliographical and Historical Notes
VII: Conclusions
	Chapter 28: Philosophy, Ethics, and Safety of AI
		28.1 The Limits of AI
		28.2 Can Machines Really Think?
		28.3 The Ethics of AI
		Summary
		Bibliographical and Historical Notes
	Chapter 29: The Future of AI
		29.1 AI Components
		29.2 AI Architectures
Appendixes
	Appendix A: Mathematical Background
		A.1 Complexity Analysis and O() Notation
		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
	A
	B
	C
	D
	E
	F
	G
	H
	I
	J
	K
	L
	M
	N
	O
	P
	Q
	R
	S
	T
	U
	V
	W
	X
	Y
	Z