Artificial Intelligence: A Modern Approach, 3/e (IE-Paperback)

Stuart Russell , Peter Norvig

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Features

  • Nontechnical learning material.
    *Provides a simple overview of major concepts, uses a nontechnical language to help increase understanding. Makes the book accessible to a broader range of students.
     
  • The Internet as a sample application for intelligent systems - Examples of logical reasoning, planning, and natural language processing using Internet agents.
    *Promotes student interest with interesting, relevant exercises.
     
  • Increased coverage of material - New or expanded coverage of 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.
    *Brings students up to date on the latest technologies, and presents concepts in a more unified manner.
     
  • Updated and expanded exercises - 30% of the exercises are revised or NEW.
     
  • More Online Software.
    *Allows many more opportunities for student projects on the web.
     
  • A unified, agent-based approach to AI - Organizes the material around the task of building intelligent agents.
    *Shows students how the various subfields of AI fit together to build actual, useful programs.
     
  • Comprehensive, up-to-date coverage - Includes a unified view of the field organized around the rational decision making paradigm.
     
  • A flexible format.
    *Makes the text adaptable for varying instructors' preferences.
     
  • In-depth coverage of basic and advanced topics.
    *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.
    *Gives instructors and students a choice of projects; reading and running the code increases understanding.

    Table of Contents

Part I Artificial Intelligence
Chapter 1 Introduction
Chapter 2 Intelligent Agents
Part II Problem-solving
Chapter 3 Solving Problems by Searching
Chapter 4: Beyond Classical Search
Chapter 5 Adversarial Search
Chapter 6 Constraint Satisfaction Problems
Part III Knowledge, Reasoning, and Planning
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
Part IV Uncertain Knowledge and Reasoning
Chapter 13 Quantifying Uncertainty
Chapter 14 Probabilistic Reasoning
Chapter 15 Probabilistic Reasoning over Time
Chapter 16 Making Simple Decisions
Chapter 17 Making Complex Decisions
Part V Learning
Chapter 18 Learning from Observations
Chapter 19 Knowledge in Learning
Chapter 20 Learning Probabilistic Models
Chapter 21 Reinforcement Learning
Part VI Communicating, Perceiving, and Acting
Chapter 22 Natural Language Processing
Chapter 23 Natural Language for Communication
Chapter 24 Perception
Chapter 25 Robotics
Part VII Conclusions
Chapter 26 Philosophical Foundations
Chapter 27 AI: The Present and Future