Artificial Intelligence Structures and Strategies for Complex Problem Solving 5th edition by George Luger ISBN 0321263189 978-0321263186

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ISBN 10: 0321263189
ISBN 13: 978-0321263186
Author: George Luger

The fifth edition of this book continues to provide a balanced perspective on the language schools, theories, and applications of artificial intelligence. These diverse branches are unified through detailed discussions of AI’s theoretical foundations. The book is broken down into six parts to provide readers complete coverage of AI. It begins by introducing AI concepts, moves into a discussion on the research tools needs for AI problem solving, and then demonstrates representations for AI and knowledge-sensitive problem solving. The second half of the book offers an extensive presentation of issues in machine learning, continues presenting important AI application areas, and presents Lisp and Prolog to the reader. This book is appropriate for programmers both as an introduction to and a reference of the theoretical foundations of artificial intelligence.

Artificial Intelligence: Structures and Strategies for Complex Problem Solving 5th Table of contents:

Preface

• Acknowledgments
• About the Author

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Part I: Introduction to Artificial Intelligence

Chapter 1: Introduction to Artificial Intelligence
1.1. What is Artificial Intelligence?
1.2. History and Evolution of AI
1.3. Foundations of AI
1.4. The Role of Algorithms in AI
1.5. The AI Problem-Solving Process
1.6. The Structure of the Book
1.7. Modern AI Applications

Chapter 2: Intelligent Agents
2.1. Definition and Types of Intelligent Agents
2.2. Rationality and the Structure of Agents
2.3. The Structure of a Problem-Solving Agent
2.4. Agent Environments
2.5. The Environment as a State Space
2.6. Task Environment and Task Complexity
2.7. Agent Architectures

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Part II: Problem Solving

Chapter 3: Problem-Solving Strategies
3.1. Problem Space and State Space
3.2. Problem Formulation
3.3. Problem Solving Techniques
3.4. Search Strategies and Algorithms
3.5. Uninformed Search Strategies (Breadth-First, Depth-First, etc.)
3.6. Informed Search Strategies (Heuristic Search)
3.7. Optimality and Efficiency of Search Algorithms
3.8. Comparison of Search Strategies

Chapter 4: Searching Techniques
4.1. Search Space and Search Trees
4.2. Blind Search Techniques
4.3. Heuristic Search Methods
4.4. A* Search Algorithm
4.5. Best-First Search
4.6. Local Search Algorithms and Hill Climbing
4.7. Simulated Annealing
4.8. Genetic Algorithms and Evolutionary Strategies

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Part III: Knowledge Representation and Reasoning

Chapter 5: Knowledge Representation
5.1. What is Knowledge Representation?
5.2. Types of Knowledge
5.3. Representation Schemes
5.4. Logic-Based Representation
5.5. Semantic Networks and Frames
5.6. Propositional Logic
5.7. First-Order Logic (Predicate Logic)
5.8. Knowledge Representation in Expert Systems
5.9. Ontologies and the Semantic Web

Chapter 6: Reasoning with Logic
6.1. The Process of Logical Reasoning
6.2. Inference and Deduction
6.3. Forward and Backward Chaining
6.4. Resolution and Proofs
6.5. Modus Ponens and Other Inference Rules
6.6. Uncertainty in Knowledge and Reasoning
6.7. Nonmonotonic Reasoning

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Part IV: Learning and Adaptation

Chapter 7: Machine Learning
7.1. Types of Machine Learning
7.2. Supervised vs. Unsupervised Learning
7.3. Learning from Examples
7.4. Decision Trees and Rule-Based Learning
7.5. Neural Networks
7.6. Support Vector Machines
7.7. Reinforcement Learning
7.8. Evaluation and Validation of Machine Learning Models

Chapter 8: Learning from Experience
8.1. The Concept of Learning from Experience
8.2. Supervised Learning Algorithms
8.3. Unsupervised Learning Techniques
8.4. Association Rules and Frequent Itemsets
8.5. Clustering and Classification
8.6. Reinforcement Learning Algorithms

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Part V: Advanced AI Topics

Chapter 9: Natural Language Processing
9.1. Introduction to NLP
9.2. Linguistic Components: Syntax, Semantics, and Pragmatics
9.3. Language Models and Parsing
9.4. Text Mining and Information Retrieval
9.5. Speech Recognition
9.6. Machine Translation
9.7. Sentiment Analysis

Chapter 10: Expert Systems
10.1. What is an Expert System?
10.2. Components of Expert Systems
10.3. Knowledge Acquisition and Representation
10.4. Inference Engines
10.5. Rule-Based Systems
10.6. Forward and Backward Chaining in Expert Systems
10.7. Case-Based Reasoning
10.8. Expert System Applications and Limitations

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Part VI: AI in Action

Chapter 11: Robotics and Perception
11.1. Introduction to Robotics
11.2. Sensor Technologies and Perception
11.3. Mobile Robots and Navigation
11.4. Robot Architecture and Control
11.5. Perception and Vision Systems
11.6. Applications of Robotics in Industry

Chapter 12: Artificial Intelligence in Games
12.1. Game Playing and Search Algorithms
12.2. Minimax Algorithm and Alpha-Beta Pruning
12.3. Strategies for Game Playing
12.4. The Role of AI in Video Games
12.5. AI in Board Games and Puzzles
12.6. Real-Time Game AI

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Part VII: Philosophical and Ethical Issues in AI

Chapter 13: Ethics and Philosophy of AI
13.1. Ethical Implications of AI
13.2. The Debate Over Machine Consciousness
13.3. AI and the Turing Test
13.4. AI and Privacy Concerns
13.5. AI and Employment
13.6. The Future of AI: Challenges and Opportunities

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Glossary

Index

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