2002 ArtificialIntelligenceAModernApproach

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Subject Headings: Artificial Intelligence Textbook.



Table of Contents

Part I Artificial Intelligence

    1 Introduction
    2 Intelligent Agents

Part II Problem Solving

    3 Solving Problems by Searching
    4 Informed Search and Exploration
    5 Constraint Satisfaction Problems (pdf)
    6 Adversarial Search

Part III Knowledge and Reasoning

    7 Logical Agents (pdf)
    8 First-Order Logic
    9 Inference in First-Order Logic
   10 Knowledge Representation

Part IV Planning

   11 Planning (pdf)
   12 Planning and Acting in the Real World

Part V Uncertain Knowledge and Reasoning

   13 Uncertainty
   14 Probabilistic Reasoning
   15 Probabilistic Reasoning Over Time
   16 Making Simple Decisions
   17 Making Complex Decisions

Part VI Learning

   18 Learning from Observations
   19 Knowledge in Learning
   20 Statistical Learning Methods (pdf)
   21 Reinforcement Learning

Part VII Communicating, Perceiving, and Acting

   22 Communication
   23 Probabilistic Language Processing
   24 Perception
   25 Robotics

Part VIII Conclusions

   26 Philosophical Foundations
   27 AI: Present and Future


  • Artificial Intelligence (AI) is a big field, and this is a big book. We have tried to explore the full breadth of the field, which encompasses logic, probability, and continuous mathematics; perception, reasoning, learning, and action; and everything from microelectronic devices to robotic planetary explorers. The book is also big because we go into some depth in presenting results, although we strive to cover only the most central ideas in the main part of each chapter. Pointers are given to further results in the bibliographical notes at the end of each chapter.
  • The subtitle of this book is "A Modern Approach." The intended meaning of this rather empty phrase is that we have tried to synthesize what is now known into a common framework, rather than trying to explain each subfield of AI in its own historical context. We apologize to those whose subfields are, as a result, less recognizable than they might otherwise have been.
  • The main unifying theme is the idea of an intelligent agent. We define AI as the study of agents that receive percepts from the environment and perform actions. Each such agent implements a function that maps percept sequences to actions, and we cover different ways to represent these functions, such as production systems, reactive agents, real-time conditional planners, neural networks, and decision-theoretic systems. We explain the role of learning as extending the reach of the designer into unknown environments, and we show how that role constrains agent design, favoring explicit knowledge representation and reasoning. We treat robotics and vision not as independently defined problems, but as occurring in the service of achieving goals. We stress the importance of the task environment in determining the appropriate agent design.
  • Our primary aim is to convey the ideas that have emerged over the past fifty years of AI research and the past two millenia of related work. We have tried to avoid excessive formality in the presentation of these ideas while retaining precision. Wherever appropriate, we have included pseudocode algorithms to make the ideas concrete; our pseudocode is described briefly in Appendix B. Implementations in several programming languages are available on the book's Web site, aima.cs.berkeley.edu.
  • This book is primarily intended for use in an undergraduate course or course sequence. It can also be used in a graduate-level course (perhaps with the addition of some of the primary sources suggested in the bibliographical notes). Because of its comprehensive coverage and large number of detailed algorithms, it is useful as a primary reference volume for AI graduate students and professionals wishing to branch out beyond their own subfield. The only prerequisite is familiarity with basic concepts of computer science (algorithms, data structures, complexity) at a sophomore level. Freshman calculus is useful for understanding neural networks and statistical learning in detail. Some of the required mathematical background is supplied in Appendix A.


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2002 ArtificialIntelligenceAModernApproachPeter Norvig
Stuart J. Russell
Artificial Intelligence: a modern approach (2nd edition)2002