Artificial Intelligence

1 Introduction

1.1 What is AI?

1.2 Approaches to Artificial Intelligence

1.3 Brief History of AI

1.4 Plan of the Book

1.5 Additional Readings and Discussion



I Reactive Machines

2 Stimulus-Response Agents

2.1 Perception and Action

2.1.1 Perception

2.1.2 Action

2.1.3 Boolean Algebra

2.1.4 Classes and Forms of Boolean Functions

2.2 Representing and Implementing Action Functions

2.2.1 Production Systems

2.2.2 Networks

2.2.3 The Subsumption Architecture

2.3 Additional Readings and Discussion



3 Neural Networks

3.1 Introduction

3.2 Training Single TLUs

3.2.1 TLU Geometry

3.2.2 Augmented Vectors

3.2.3 Gradient Descent Methods

3.2.4 The Widrow-Hoff Procedure

3.2.5 The Generalized Delta Procedure

3.2.6 The Error-Correction Procedure

3.3 Neural Networks

3.3.1 Motivation

3.3.2 Notation

3.3.3 The Backpropagation Method

3.3.4 Computing Weight Changes in the Final Layer

3.3.5 Computing Changes to the Weights in Intermediate Layers

3.4 Generalization, Accuracy, and Overfitting

3.5 Additional Readings and Discussion



4 Machine Evolution

4.1 Evolutionary Computation

4.2 Genetic Programming

4.2.1 Program Representation in GP

4.2.2 The GP Process

4.2.3 Evolving a Wall-Following Robot

4.3 Additional Readings and Discussion



5 State Machines

5.1 Representing the Environment by Feature Vectors

5.2 Elman Networks

5.3 Iconic Representations

5.4 Blackboard Systems

5.5 Additional Readings and Discussion



6 Robot Vision

6.1 Introduction

6.2 Steering a Van

6.3 Two Stages of Robot Vision

6.4 Image Processing

6.4.1 Averaging

6.4.2 Edge Enhancement

6.4.3 Combining Edge-Enhancement with Averaging

6.4.4 Region Finding

6.4.5 Using Image Attributes other than Intensity

6.5 Scene Analysis

6.5.1 Interpreting Lines and Curves in the Image

6.5.2 Model-Based Vision

6.6 Stereo Vision

6.7 Additional Readings and Discussion



II Search in State Spaces

7 Agents that Plan

7.1 Memory Versus Computation

7.2 State-Space Graphs

7.3 Searching Explicit State Spaces

7.4 Feature-Based State Spaces

7.5 Graph Notation 7.6 Additional Readings and Discussion



8 Uninformed Search

8.1 Formulating the State Space

8.2 Components of Implicit State-Space Graphs

8.3 Breadth-First Search

8.4 Depth-First or Bracktracking Search

8.5 Iterative Deepening

8.6 Additional Readings and Discussion



9 Heuristic Search

9.1 Using Evaluation Functions

9.2 A General Graph-Searching Algorithm

9.2.1 Algorithm A

9.2.2 Admissibility of A

9.2.3 The Consistency (or Monotone) Condition

9.2.4 Iterative-Deepening A

9.2.5 Recursive Best-First Search

9.3 Heuristic Functions and Search Efficiency

9.4 Additional Readings and Discussion



10 Planning, Acting, and Learning

10.1 The Sense/Plan/Act Cycle

10.2 Approximate Search

10.2.1 Island-Driven Search

10.2.2 Hierarchical Search

10.2.3 Limited-Horizon Search

10.2.4 Cycles

10.2.5 Building Reactive Procedures

10.3 Learning Heuristic Functions

10.3.1 Explicit Graphs

10.3.2 Implicit Graphs

10.4 Rewards Instead of Goals

10.5 Additional Readings and Discussion



11 Alternative Search Formulations and Applications

11.1 Assignment Problems

11.2 Constructive Methods

11.3 Heuristic Repair

11.4 Function Optimization



12 Adversarial Search

12.1 Two-Agent Games

12.2 The Minimax Procedure

12.3 The Alpha-Beta Procedure

12.4 The Search Efficiency of the Alpha-Beta Procedure

12.5 Other Important Matters

12.6 Games of Chance

12.7 Learning Evaluation Functions

12.8 Additional Readings and Discussion



III Knowledge Representation and Reasoning

13 The Propositional Calculus

13.1 Using Constraints on Feature Values

13.2 The Language

13.3 Rules of Inference

13.4 Definition of Proof

13.5 Semantics

13.5.1 Interpretations

13.5.2 The Propositional Truth Table

13.5.3 Satisfiability and Models

13.5.4 Validity

13.5.5 Equivalence

13.5.6 Entailment

13.6 Soundness and Completeness

13.7 The PSAT Problem

13.8 Other Important Topics

13.8.1 Language Distinctions

13.8.2 Metatheorems

13.8.3 Associative Laws

13.8.4 Distributive Laws



14 Resolution in The Propositional Calculus

14.1 A New Rule of Inference: Resolution

14.1.1 Clauses as wwf

14.1.2 Resolution on Clauses

14.1.3 Soundness of Resolution

14.2 Converting Arbitrary wffs to Conjunctions of Clauses

14.3 Resolution Refutations

14.4 Resolution Refutation Search Strategies

14.4.1 Ordering Strategies

14.4.2 Refinement Strategies

14.5 Horn Clauses



15 The Predicate Calculus

15.1 Motivation

15.2 The Language and its Syntax

15.3 Semantics

15.3.1 Worlds

15.3.2 Interpretations

15.3.3 Models and Related Notions

15.3.4 Knowledge

15.4 Quantification

15.5 Semantics of Quantifiers

15.5.1 Universal Quantifiers

15.5.2 Existential Quantifiers

15.5.3 Useful Equivalences

15.5.4 Rules of Inference

15.6 Predicate Calculus as a Language for Representing Knowledge

15.6.1 Conceptualizations

15.6.2 Examples

15.7 Additional Readings and Discussion



16 Resolution in the Predicate Calculus

16.1 Unification

16.2 Predicate-Calculus Resolution

16.3 Completeness and Soundness

16.4 Converting Arbitrary wffs to Clause Form

16.5 Using Resolution to Prove Theorems

16.6 Answer Extraction

16.7 The Equality Predicate

16.8 Additional Readings and Discussion



17 Knowledge-Based Systems

17.1 Confronting the Real World

17.2 Reasoning Using Horn Clauses

17.3 Maintenance in Dynamic Knowledge Bases

17.4 Rule-Based Expert Systems

17.5 Rule Learning

17.5.1 Learning Propositional Calculus Rules

17.5.2 Learning First-Order Logic Rules

17.5.3 Explanation-Based Generalization

17.6 Additional Readings and Discussion



18 Representing Commonsense Knowledge

18.1 The Commonsense World

18.1.1 What is Commonsense Knowledge?

18.1.2 Difficulties in Representing Commonsense Knowledge

18.1.3 The Importance of Commonsense Knowledge

18.1.4 Research Areas

18.2 Time

18.3 Knowledge Representation by Networks

18.3.1 Taxonomic Knowledge

18.3.2 Semantic Networks

18.3.3 Nonmonotonic Reasoning in Semantic Networks

18.3.4 Frames

18.4 Additional Readings and Discussion



19 Reasoning with Uncertain Information

19.1 Review of Probability Theory

19.1.1 Fundamental Ideas

19.1.2 Conditional Probabilities

19.2 Probabilistic Inference

19.2.1 A General Method

19.2.2 Conditional Independence

19.3 Bayes Networks

19.4 Patterns of Inference in Bayes Networks

19.5 Uncertain Evidence

19.6 D-Seperation

19.7 Probabilistic Inference in Polytrees

19.7.1 Evidence Above

19.7.2 Evidence Below

19.7.3 Evidence Above and Below

19.7.4 A Numerical Example

19.8 Additional Readings and Discussion



20 Learning and Acting with Bayes Nets

20.1 Learning Bayes Nets

20.1.1 Known Network Structure

20.1.2 Learning Network Structure

20.2 Probabilistic Inference and Action

20.2.1 The General Setting

20.2.2 An Extended Example

20.2.3 Generalizing the Example

20.3 Additional Readings and Discussion



IV Planning Method Based on Logic

21 The Situation Calculus

21.1 Reasoning about States and Actions

21.2 Some Difficulties

21.2.1 Frame Axioms

21.2.2 Qualifications

21.2.3 Ramifications

21.3 Generating Plans

21.4 Additional Reading and Discussion



22 Planning

22.1 STRIPS Planning Systems

22.1.1 Describing States and Goals

22.1.2 Forward Search Methods

22.1.3 Recursive STRIPS

22.1.4 Plans with Runtime Conditionals

22.1.5 The Sussman Anomaly

22.1.6 Backward Search Methods

22.2 Plan Spaces and Partial-Order Planning

22.3 Hierarchical Planning

22.3.1 ABSTRIPS

22.3.2 Combining Hierarchical and Partial-Order Planning

22.4 Learning Plans'

22.5 Additional Readings and Discussion



V Communication and Integration

23 Multiple Agents

23.1 Interacting Agents

23.2 Models of Other Agents

23.2.1 Varieties of Models

23.2.2 Simulation Strategies

23.2.3 Simulated Databases

23.2.4 The Intentional Stance

23.3 A Modal Logic of Knowledge

23.3.1 Modal Operators

23.3.2 Knowledge Axioms

23.3.3 Reasoning about Other Agents' Knowledge

23.3.4 Predicting Actions of Other Agents

23.4 Additional Readings and Discussion



24 Communication Among Agents

24.1 Speech Acts

24.1.1 Planning Speech Acts

24.1.2 Implementing Speech Acts

24.2 Understanding Language Strings

24.2.1 Phrase-Structure Grammars

24.2.2 Semantic Analysis

24.2.3 Expanding the Grammar

24.3 Efficient Communication

24.3.1 Use of Context

24.3.2 Use of Knowledge to Resolve Ambiguities

24.4 Natural Language Processing

24.5 Additional Readings and Discussion

25 Agent Architectures

25.1 Three-Level Architectures

25.2 Goal Arbitration

25.3 The Triple-Tower Architecture

25.4 Bootstrapping

25.5 Additional Readings and Discussion

Intelligent agents are employed as the central characters in this new introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents. The book provides a refreshing and motivating new synthesis of the field by one of AI's master expositors and leading researchers. Artificial Intelligence: A New Synthesis takes the reader on a complete tour of this intriguing new world of AI.

• An evolutionary approach provides a unifying theme
• Thorough coverage of important AI ideas, old and new
• Frequent use of examples and illustrative diagrams
• Extensive coverage of machine learning methods throughout the text
• Citations to over 500 references
• Comprehensive index