Artificial Intelligence : A Modern Approach / Stuart J. Russell and Peter Norvig

It includes Appendix, Bibliography and Index

This edition captures the changes that have taken place in the field of artificial intelligence (AI) since the last edition in 2003. There have been important applications of AI technology, such as the widespread deployment of practical speech recognition, machine translation, autonomous vehicles, and household robotics. There have been algorithmic landmarks, such as the solution of the game of checkers. There has also been a great deal of theoretical progress, particularly in areas such as probabilistic reasoning, machine learning, and computer vision.

Table of Content

Chapter 1 Introduction
Chapter 2 Intelligent Agents
Chapter 3 Solving Problems by Searching
Chapter 4 Beyond Classical Search
Chapter 5 Adversarial Search
Chapter 6 Constraint Satisfaction Problems
Chapter 7 Logical Agents
Chapter 8 First-Order Logic
Chapter 9 Inference in First-Order Logic
Chapter 10 Classical Planning
Chapter 11 Planning and Acting in the Real World
Chapter 12 Knowledge Representation
Chapter 13 Quantifying Uncertainty
Chapter 14 Probabilistic Reasoning
Chapter 15 Probabilistic Reasoning over Time
Chapter 16 Making Simple Decisions
Chapter 17 Making Complex Decisions
Chapter 18 Learning from Examples
Chapter 19 Knowledge in Learning
Chapter 20 Learning Probabilistic Models
Chapter 21 Reinforcement Learning
Chapter 22 Natural Language Processing
Chapter 23 Natural Language for Communication
Chapter 24 Perception
Chapter 25 Robotics
Chapter 26 Philosophical Foundations



Salient Features

• Nontechnical learning material provides a simple overview of major concepts
• Expanded coverage of topics such as constraint satisfaction, local search planning methods, multi-agent systems, game theory, statistical natural language processing and uncertain reasoning over time
• More detailed descriptions of algorithms for probabilistic inference, fast propositional inference, probabilistic learning approaches including EM, and other topics
• Updated and expanded exercises
• A unified, agent-based approach to AI — Organizes the material around the task of building intelligent agents
• Comprehensive, up-to-date coverage — Includes a unified view of the field organized around the rational decision making paradigm
• In-depth coverage of basic and advanced topics which provides students with a basic understanding of the frontiers of AI without compromising complexity and depth.
• Pseudo-code versions of the major AI algorithms are presented in a uniform fashion, and Actual Common Lisp and Python implementations of the presented algorithms are available via the Internet
Results