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ISBN 10: 1558604677
ISBN 13: 978-1558604674
Author: Nils Nilsson
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
Artificial Intelligence A New Synthesis 1st Table of contents:
Chapter 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
Exercises
Part I: Reactive Machines
Chapter 2. Stimulus-Response Agents
2.1 Perception and Action
2.2 Representing and Implementing Action Functions
2.3 Additional Readings and Discussion
Exercises
Chapter 3. Neural Networks
3.1 Introduction
3.2 Training Single TLUs
3.3 Neural Networks
3.4 Generalization, Accuracy, and Overfitting
3.5 Additional Readings and Discussion
Exercises
Chapter 4. Machine Evolution
4.1 Evolutionary Computation
4.2 Genetic Programming
4.3 Additional Readings and Discussion
Exercises
Chapter 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
Exercises
Chapter 6. Robot Vision
6.1 Introduction
6.2 Steering an Automobile
6.3 Two Stages of Robot Vision
6.4 Image Processing
6.5 Scene Analysis
6.6 Stereo Vision and Depth Information
6.7 Additional Readings and Discussion
Exercises
Part II: Search in State Spaces
Chapter 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
Exercises
Chapter 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 Backtracking Search
8.5 Iterative Deepening
8.6 Additional Readings and Discussion
Exercises
Chapter 9. Heuristic Search
9.1 Using Evaluation Functions
9.2 A General Graph-Searching Algorithm
9.3 Heuristic Functions and Search Efficiency
9.4 Additional Readings and Discussion
Exercises
Chapter 10. Planning, Acting, and Learning
10.1 The Sense/Plan/Act Cycle
10.2 Approximate Search
10.3 Learning Heuristic Functions
10.4 Rewards Instead of Goals
10.5 Additional Readings and Discussion
Exercises
Chapter 11. Alternative Search Formulations and Applications
11.1 Assignment Problems
11.2 Constructive Methods
11.3 Heuristic Repair
11.4 Function Optimization
Exercises
Chapter 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
Exercises
Part III: Knowledge Representation and Reasoning
Chapter 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
13.6 Soundness and Completeness
13.7 The PSAT Problem
13.8 Other Important Topics
Exercises
Chapter 14. Resolution in the Propositioned Calculus
14.1 A New Rule of Inference: Resolution
14.2 Converting Arbitrary wffs to Conjunctions of Clauses
14.3 Resolution Refutations
14.4 Resolution Refutation Search Strategies
14.5 Horn Clauses
Exercises
Chapter 15. The Predicate Calculus
15.1 Motivation
15.2 The Language and Its Syntax
15.3 Semantics
15.4 Quantification
15.5 Semantics of Quantifiers
15.6 Predicate Calculus as a Language for Representing Knowledge
15.7 Additional Readings and Discussion
Exercises
Chapter 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
Exercises
Chapter 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
17.6 Additional Readings and Discussion
Exercises
Chapter 18. Representing Commonsense Knowledge
18.1 The Commonsense World
18.2 Time
18.3 Knowledge Representation by Networks
18.4 Additional Readings and Discussion
Exercises
Chapter 19. Reasoning with Uncertain Information
19.1 Review of Probability Theory
19.2 Probabilistic Inference
19.3 Bayes Networks
19.4 Patterns of Inference in Bayes Networks
19.5 Uncertain Evidence
19.6 D-Separation
19.7 Probabilistic Inference in Polytrees
19.8 Additional Readings and Discussion
Exercises
Chapter 20. Learning and Acting with Bayes Nets
20.1 Learning Bayes Nets
20.2 Probabilistic Inference and Action
20.3 Additional Readings and Discussion
Exercises
Part IV: Planning Methods Based on Logic
Chapter 21. The Situation Calculus
21.1 Reasoning about States and Actions
21.2 Some Difficulties
21.3 Generating Plans
21.4 Additional Readings and Discussion
Exercises
Chapter 22. Planning
22.1 STRIPS Planning Systems
22.2 Plan Spaces and Partial-Order Planning
22.3 Hierarchical Planning
22.4 Learning Plans
22.5 Additional Readings and Discussion
Exercises
Part V: Communication and Integration
Chapter 23. Multiple Agents
23.1 Interacting Agents
23.2 Models of Other Agents
23.3 A Modal Logic of Knowledge
23.4 Additional Readings and Discussion
Exercises
Chapter 24. Communication among Agents
24.1 Speech Acts
24.2 Understanding Language Strings
24.3 Efficient Communication
24.4 Natural Language Processing
24.5 Additional Readings and Discussion
Exercises
Chapter 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
Exercises
Bibliography
Index
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