Paradigms of AI Programming is the first text to teach advanced Common Lisp techniques in the context of building major AI systems. By reconstructing authentic, complex AI programs using state-of-the-art Common Lisp, the book teaches students and professionals how to build and debug robust practical programs, while demonstrating superior programming style and important AI concepts. The author strongly emphasizes the practical performance issues involved in writing real working programs of significant size. Chapters on troubleshooting and efficiency are included, along with a discussion of the fundamentals of object-oriented programming and a description of the main CLOS functions. This volume is an excellent text for a course on AI programming, a useful supplement for general AI courses and an indispensable reference for the professional programmer.

Table of Contents

Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp
by Peter Norvig
  • Preface
  • Why Lisp? Why Common Lisp?
  • Outline of the Book
  • How to use This Book
  • Supplementary Texts and Reference Books
  • A Note on Exercises
  • Acknowledgments
  • Part I Introduction to Common Lisp
  • 1 Introduction to Lisp
    • 1.1 Symbolic Computation
    • 1.2 Variables
    • 1.3 Special Forms
    • 1.4 Lists
    • 1.5 Defining New Functions
    • 1.6 Using Functions
    • 1.7 Higher-Order Functions
    • 1.8 Other Data Types
    • 1.9 Summary: The Lisp Evaluation Rule
    • 1.10 What Makes Lisp Different?
    • 1.11 Exercises
    • 1.12 Answers
  • 2 A Simple Lisp Program
    • 2.1 A Grammar for a Subset of English
    • 2.2 A Straightforward Solution
    • 2.3 A Rule-Based Solution
    • 2.4 Two paths to Follow
    • 2.5 Changing the Grammar without Changing the Program
    • 2.6 Using the Same Data for Several Programs
    • 2.7 Exercises
    • 2.8 Answers
  • 3 Overview of Lisp
    • 3.1 A Guide to Lisp Style
    • 3.2 Special Forms
      • Special Forms for Definitions
      • Special Forms for Conditionals
      • Special Forms for Dealing with Variables and Places
      • Function


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© 1992
Morgan Kaufmann
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