Talk:Artificial intelligence/Textbook survey

This is a survey of major AI textbooks and a few academic course listings, designed to determine for Wikipedia what topics are essential to an introduction to artificial intelligence. This is intended to help the central articles about artificial intelligence to pass the featured article criteria. It should be noted that there is a great deal of consensus among experts on what subjects constitute the whole field of AI research.

Textbooks
These are listed on the list of textbooks at AI Topics, which also lists their relative popularity. These are the four most popular textbooks published since 1998 (i.e. in the ten years before this survey was done.)

Russell & Norvig (standard AI textbook)
Chapters:
 * 1 Introduction History of AI, some philosophy of AI
 * 2 Intelligent agent paradigm
 * 3-6 Search
 * 7-9 Logic
 * 10 Knowledge representation
 * 11-12 Planning
 * 13-17 Uncertain reasoning
 * 18-21 Learning
 * 22-23 Natural language processing (they call "communication")
 * 24 Perception
 * 25 Robotics
 * 26 Philosophy of AI
 * 27 future of AI

Nilsson

 * 1 Introduction

I Reactive Machines
 * 2 Stimulus-Response Agents
 * 3 Neural Networks
 * 4 Machine Evolution
 * 5 State Machines
 * 6 Robot Vision

II Search in State Spaces
 * 7-9 search, uninformed, heuristic
 * 10 Planning, Acting, and Learning chapter is actually mostly about search, I think...
 * 11 Alternative Search Formulations and Applications
 * 12 Adversarial Search

III Knowledge Representation and Reasoning
 * 13-16 The Propositional, Predicate Calculus and resolution
 * 17 Knowledge-Based Systems
 * 18 Representing Commonsense Knowledge
 * 19 Reasoning with Uncertain Information
 * 20 Learning and Acting with Bayes Nets

IV Planning Method Based on Logic
 * 21 The Situation Calculus
 * 22 Planning

V Communication and Integration
 * 23 Multiple Agents
 * 24 Communication Among Agents Natural Language Processing
 * 25 Agent Architectures

Luger & Stubblefield

 * 1 ARTIFICIAL INTELLIGENCE: ITS ROOTS AND SCOPE 1
 * 2 THE PREDICATE CALCULUS 45
 * 3-4 STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH 79 (including Hill Climbing and Dynamic Programming)
 * 5 STOCHASTIC METHODS 165
 * 6 CONTROL AND IMPLEMENTATION OF STATE SPACE SEARCH 193
 * 7 KNOWLEDGE REPRESENTATION 227
 * 8 STRONG METHOD PROBLEM SOLVING (Expert systems) 277
 * 9 REASONING IN UNCERTAIN SITUATIONS 333
 * 10-12 MACHINE LEARNING: SYMBOL-BASED 387 / CONNECTIONIST 453 / SOCIAL AND EMERGENT 507 (including: Genetic, classifier, artificial life)
 * 13 AUTOMATED REASONING 547
 * 14 UNDERSTANDING NATURAL LANGUAGE 591
 * 15 PROLOG 636
 * 16 AN INTRODUCTION TO LISP 723
 * 17 ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY 823
 * 17 ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY 823

Poole & Macworth

 * Chapter 1 Computational Intelligence and Knowledge introduction
 * Chapter 2 A Representation and Reasoning System forward and backward chaining
 * Chapter 3 Using Definite Knowledge ''includes databases and natural language
 * Chapter 4 Searching includes standard state space searches, dynamic programming, constraint satisfation, hill climbing, "randomization algortihms" and genetic algorithms
 * Chapter 5 Representing Knowledge
 * Chapter 6 Knowledge Engineering,
 * Chapter 7 Beyond Definite Knowledge includes first order logic, proof systems
 * Chapter 8 Actions and Planning
 * Chapter 9 Assumption-Based Reasoning, default reasoning, abduction
 * Chapter 10 Using Uncertain Knowledge
 * Chapter 11 Learning
 * Chapter 12 Building Situated Robots

Rich & Knight

 * What is Artificial Intelligence?
 * Problems, Problem Spaces, and Search. Heuristic Search Techniques.
 * Knowledge Representation. Knowledge Representation Issues.
 * Using Predicate Logic. Representing Knowledge Using Rules.
 * Symbolic Reasoning Under Uncertainty. Statistical Reasoning.
 * Weak Slot-and-Filler Structures. Strong Slot-and-Filler Structures. Knowledge Representation Summary.
 * Game Playing.
 * Planning.
 * Understanding.
 * Natural Language Processing.
 * Parallel and Distributed AI.
 * Learning.
 * Connectionist Models.
 * Common Sense.
 * Expert Systems.
 * Perception and Action.
 * Conclusion.

Cawsey

 * Introduction.
 * Knowledge Representation and Inference.
 * Expert Systems.
 * Using Search in Problem Solving.
 * Natural Language Processing.
 * Vision.
 * Machine Learning and Neural Networks.
 * Agents and Robots.

Murray

 * Introduction.
 * 1-34 Modules of the AI Mind; Exercises.
 * JavaScript source code of the tutorial AI Mind.

Poole and Mackworth (2010)
Artificial Intelligence: Foundations of Computational Agents
 * I Agents in the World: What Are Agents and How Can They Be Built?
 * 1 Artificial Intelligence and Agents
 * 2 Agent Architectures and Hierarchical Control
 * II Representing and Reasoning
 * 3 States and Searching
 * 4 Features and Constraints
 * 5 Propositions and Inference
 * 6 Reasoning Under Uncertainty
 * III Learning and Planning
 * 7 Learning: Overview and Supervised Learning
 * 8 Planning with Certainty
 * 9 Planning Under Uncertainty
 * 10 Multiagent Systems
 * 11 Beyond Supervised Learning
 * IV Reasoning About Individuals and Relations
 * 12 Individuals and Relations
 * 13 Ontologies and Knowledge-Based Systems
 * 14 Relational Planning, Learning, and Probabilistic Reasoning
 * V The Big Picture
 * 15 Retrospect and Prospect

Cambridge Handbook of Artificial Intelligence (2014)

 * Part I: Foundations
 * 1. History, motivations, and core themes
 * 2. Philosophical foundations
 * 3. Philosophical challenges
 * Part II: Architectures
 * 4. GOFAI
 * 5. Connectionism and neural networks
 * 6. Dynamical systems and embedded cognition
 * Part III: Dimensions
 * 7. Learning
 * 8. Perception and computer vision
 * 9. Reasoning and decision making
 * 10. Language and communication
 * 11. Actions and agents
 * 12. Artificial emotions and machine consciousness
 * Part IV: Extensions
 * 13. Robotics
 * 14. Artificial life
 * 15. The ethics of artificial intelligence

ACM classification

 * I.2.0 General
 * I.2.1 Applications and Expert Systems (H.4, J) considered in this in the section "Applications"
 * I.2.2 Automatic Programming (D.1.2, F.3.1, F.4.1) not considered AI by wikipedia
 * I.2.3 Deduction and Theorem Proving (F.4.1)
 * I.2.4 Knowledge Representation Formalisms and Methods (F.4.1)
 * I.2.5 Programming Languages and Software (D.3.2)
 * I.2.6 Learning (K.3.2)
 * I.2.7 Natural Language Processing
 * I.2.8 Problem Solving, Control Methods, and Search (F.2.2) control theory, dynamic programming, search, planning & scheduling
 * I.2.9 Robotics
 * I.2.10 Vision and Scene Understanding (I.4.8, I.5)
 * I.2.11 Distributed Artificial Intelligence

Sloman

 * Perception
 * Natural language processing
 * Learning
 * Planning, problem solving, automatic design
 * Varieties of reasoning
 * Study of representations (knowledge representation)
 * Memory mechanisms and techniques
 * Multi agent systems
 * Affective mechanisms
 * Robotics
 * Architectures for complete systems.
 * Search
 * Ontologies

Leake

 * Knowledge capture, representation and reasoning
 * Reasoning under uncertainty
 * Planning, Vision, and Robotics
 * Natural language processing
 * Machine Learning

Bringing it all together
This table lists (just about) every topic that appears in the title of a section or in a chapter summary of, the most popular AI textbook. Information for the other textbooks is based on their tables of contents, available online. Several topics appear more than once, in different contexts.