Efnisyfirlit

• 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
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  • O
  • P
  • Q
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