book

Artificial Intelligence

by Nils J. Nilsson

April 1998

Beginner

513 pages

14h 32m

English

Morgan Kaufmann

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1.1 What Is AI?1.2 Approaches to Artificial Intelligence1.3 Brief History of AI1.4 Plan of the Book1.5 Additional Readings and DiscussionExercises
2.1 Perception and Action2.2 Representing and Implementing Action Functions2.3 Additional Readings and DiscussionExercises

3.1 Introduction3.2 Training Single TLUs3.3 Neural Networks3.4 Generalization, Accuracy, and Overfitting3.5 Additional Readings and DiscussionExercises
4.1 Evolutionary Computation4.2 Genetic Programming4.3 Additional Readings and DiscussionExercises
5.1 Representing the Environment by Feature Vectors5.2 Elman Networks5.3 Iconic Representations5.4 Blackboard Systems5.5 Additional Readings and DiscussionExercises
6.1 Introduction6.2 Steering an Automobile6.3 Two Stages of Robot Vision6.4 Image Processing6.5 Scene Analysis6.6 Stereo Vision and Depth Information6.7 Additional Readings and DiscussionExercises
7.1 Memory Versus Computation7.2 State-Space Graphs7.3 Searching Explicit State Spaces7.4 Feature-Based State Spaces7.5 Graph Notation7.6 Additional Readings and DiscussionExercises
8.1 Formulating the State Space8.2 Components of Implicit State-Space Graphs8.3 Breadth-First Search8.4 Depth-First or Backtracking Search8.5 Iterative Deepening8.6 Additional Readings and DiscussionExercises
9.1 Using Evaluation Functions9.2 A General Graph-Searching Algorithm9.3 Heuristic Functions and Search Efficiency9.4 Additional Readings and DiscussionExercises
10.1 The Sense/Plan/Act Cycle10.2 Approximate Search10.3 Learning Heuristic Functions10.4 Rewards Instead of Goals10.5 Additional Readings and DiscussionExercises
11.1 Assignment Problems11.2 Constructive Methods11.3 Heuristic Repair11.4 Function OptimizationExercises
12.1 Two-Agent Games12.2 The Minimax Procedure12.3 The Alpha-Beta Procedure12.4 The Search Efficiency of the Alpha-Beta Procedure12.5 Other Important Matters12.6 Games of Chance12.7 Learning Evaluation Functions12.8 Additional Readings and DiscussionExercises
13.1 Using Constraints on Feature Values13.2 The Language13.3 Rules of Inference13.4 Definition of Proof13.5 Semantics13.6 Soundness and Completeness13.7 The PSAT Problem13.8 Other Important TopicsExercises
14.1 A New Rule of Inference: Resolution14.2 Converting Arbitrary wffs to Conjunctions of Clauses14.3 Resolution Refutations14.4 Resolution Refutation Search Strategies14.5 Horn ClausesExercises
15.1 Motivation15.2 The Language and Its Syntax15.3 Semantics15.4 Quantification15.5 Semantics of Quantifiers15.6 Predicate Calculus as a Language for Representing Knowledge15.7 Additional Readings and DiscussionExercises
16.1 Unification16.2 Predicate-Calculus Resolution16.3 Completeness and Soundness16.4 Converting Arbitrary wffs to Clause Form16.5 Using Resolution to Prove Theorems16.6 Answer Extraction16.7 The Equality Predicate16.8 Additional Readings and DiscussionExercises
17.1 Confronting the Real World17.2 Reasoning Using Horn Clauses17.3 Maintenance in Dynamic Knowledge Bases17.4 Rule-Based Expert Systems17.5 Rule Learning17.6 Additional Readings and DiscussionExercises
18.1 The Commonsense World18.2 Time18.3 Knowledge Representation by Networks18.4 Additional Readings and DiscussionExercises
19.1 Review of Probability Theory19.2 Probabilistic Inference19.3 Bayes Networks19.4 Patterns of Inference in Bayes Networks19.5 Uncertain Evidence19.6 D-Separation19.7 Probabilistic Inference in Polytrees19.8 Additional Readings and DiscussionExercises
20.1 Learning Bayes Nets20.2 Probabilistic Inference and Action20.3 Additional Readings and DiscussionExercises
21.1 Reasoning about States and Actions21.2 Some Difficulties21.3 Generating Plans21.4 Additional Readings and DiscussionExercises
22.1 STRIPS Planning Systems22.2 Plan Spaces and Partial-Order Planning22.3 Hierarchical Planning22.4 Learning Plans22.5 Additional Readings and DiscussionExercises
23.1 Interacting Agents23.2 Models of Other Agents23.3 A Modal Logic of Knowledge23.4 Additional Readings and DiscussionExercises
24.1 Speech Acts24.2 Understanding Language Strings24.3 Efficient Communication24.4 Natural Language Processing24.5 Additional Readings and DiscussionExercises
25.1 Three-Level Architectures25.2 Goal Arbitration25.3 The Triple-Tower Architecture25.4 Bootstrapping25.5 Additional Readings and DiscussionExercises

Overview

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.