Essentials of Artificial Intelligence - 1st Edition - ISBN: 9781558602212, 9780323139687

Essentials of Artificial Intelligence

1st Edition

Authors: Matt Ginsberg
eBook ISBN: 9780323139687
Imprint: Morgan Kaufmann
Published Date: 1st April 1993
Page Count: 430
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Description

Since its publication, Essentials of Artificial Intelligence has been adopted at numerous universities and colleges offering introductory AI courses at the graduate and undergraduate levels. Based on the author's course at Stanford University, the book is an integrated, cohesive introduction to the field. The author has a fresh, entertaining writing style that combines clear presentations with humor and AI anecdotes. At the same time, as an active AI researcher, he presents the material authoritatively and with insight that reflects a contemporary, first hand understanding of the field. Pedagogically designed, this book offers a range of exercises and examples.

Table of Contents

Essentials of Artificial Intelligence
by Matt Ginsberg
    Part I Introduction and Overview
      1 Introduction: What is AI?
        1.1 Defining Artificial Intelligence
          1.1.1 Intelligence
          1.1.2 Artifacts
          1.1.3 Construction
        1.2 What AI is About
          1.2.1 The Subfields of AI
          1.2.2 The Role of Examples in AI
        1.3 What AI Is Like
        1.4 Further Reading
        1.5 Exercises

      2 Overview
        2.1 Intelligent Action
        2.2 Search
          2.2.1 Blind Search
          2.2.2 Heuristic Search
          2.2.3 Other Issues
          2.2.4 Search: Examples
        2.3 Knowledge Representation
          2.3.1 Knowledge Representation: Examples
        2.4 Applications: Examples
        2.5 Further Reading
        2.6 Exercises
    Part II Search
    3 Blind Search
      3.1 Breadth-First Search
      3.2 Depth-First Search
      3.3 Iterative Deepening
      3.4 Iterative Broadening
      3.5 Searching Graphs
        3.5.1 Open and Closed Lists
        3.5.2 Dynamic Backtracking
      3.6 Further Reading
      3.7 Exercises

    4 Heuristic Search
      4.1 Search as Function Maximization
        4.1.1 Hill Climbing
        4.1.2 Simulated Annealing
      4.2 A
        4.2.1 Admissibility
        4.2.2 Examples
      4.3 Extensions and IDA

      4.4 Further Reading
      4.5 Exercises

    5 Adversary Search
      5.1 Assumptions
      5.2 Minimax
        5.2.1 Quiescence and Singular Extensions
        5.2.2 The Horizon Effect
      5.3 ((( Search
      5.4 Further Reading
      5.5 Exercises
Part III Knowledge Representation: Logic
6 Introduction to Knowledge Representation
    6.1 A Programming Analogy
    6.2 Syntax
    6.3 Semantics
    6.4 Soundness and Completeness
    6.5 how Hard Is Theorem Proving?
    6.6 Further Reading
    6.7 Exercises

7 Predicate Logic
    7.1 Inference Using Modus Ponens
    7.2 Horn Databases
    7.3 The Resolution Rule
    7.4 Backward Chaining Using Resolution
    7.5 Normal Form
    7.6 Further Reading
    7.7 Exercises

8 First-Order Logic
    8.1 Databases with Quantifiers
    8.2 Unification
    8.3 Skolemizing Queries
    8.4 Finding the Most General Unifier
    8.5 Modus Ponens and Horn Databases
    8.6 Resolution and Normal Form
    8.7 Further Reading
    8.8 Exercises

9 Putting Logic to Work: Control of Reasoning
    9.1 Resolution Strategies
    9.2 Compile-Time and Run-Time Control
    9.3 The Role of Metalevel Reasoning in AI
    9.4 Runtime Control of Search
      9.4.1 Lookahead
      9.4.2 The Cheapest-First Heuristic
      9.4.3 Dependency-Directed Backtracking and Backjumping
    9.5 Declarative Control of Search
    9.6 Further Reading
    9.7 Exercises
Part IV Knowledge Representation: Other Techniques
10 Assumption-Based Truth Maintenance
    10.1 Definition
    10.2 Applications
      10.2.1 Synthesis problems: Planning and Design
      10.2.2 Diagnosis
      10.2.3 Database Updates
    10.3 Implementation
    10.4 Further Reading
    10.5 Exercises

11 Nonmonotonic Reasoning
    11.1 Examples
      11.1.1 Inheritance Hierarchies
      11.1.2 The Frame Problem
      11.1.3 Diagnosis
    11.2 Definition
      11.2.1 Extensions
      11.2.2 Multiple Extensions
    11.3 Computational Problems
    11.4 Final Remarks
    11.5 Further Reading
    11.6 Exercises

12 Probability
    12.1 MYCIN and Certainty Factors
    12.2 Bayes' Rule and the Axioms of Probability
    12.3 Influence Diagrams
    12.4 Arguments For and Against Probability in AI
    12.5 Further Reading
    12.6 Exercises

13 Putting Knowledge to Work: Frames and Semantic Nets
    13.1 Introductory Examples
      13.1.1 Frames
      13.1.2 Semantic Nets
    13.2 Extensions
      13.2.1 Multiple Instances
      13.2.2 Nonunary Predicates
    13.3 Inference in Monotonic Frame Systems
    13.4 Inference in Nonmonotonic Frame Systems
    13.5 Further Reading
    13.6 Exercises
Part V AI Systems
14 Planning
    14.1 General-Purpose and Special-Purpose Planners
    14.2 Reasoning about Action
    14.3 Descriptions of Action
      14.3.1 Nondeclarative Methods
      14.3.2 Monotonic Methods
      14.3.3 Nonmonotonic Methods
    14.4 Search in Planning
      14.4.1 Hierarchical Planning
      14.4.2 Subgoal Ordering and Nonlinear Planning
      14.4.3 Subgoal Interaction and the Sussman Anomaly
    14.5 Implementing a Planner
    14.6 Further Reading
    14.7 Exercises

15 Learning
    15.1 Discovery Learning
    15.2 Inductive Learning
      15.2.1 PAC Learning
      15.2.2 Version Spaces
      15.2.3 Neural Networks
      15.2.4 ID3
    15.3 Explanation-Based Learning
    15.4 Further Reading
    15.5 Exercises

16 Vision
    16.1 Digitization
    16.2 Low-Level Processing
      16.2.1 Noise Removal
      16.2.2 Feature Detection
    16.3 Segmentation and the Hough Transform
    16.4 Recovering 3-D Information
      16.4.1 The Waltz Algorithm
      16.4.2 The 2½-D Sketch
    16.5 Active Vision
    16.6 Object and Scene Recognition
    16.7 Further Reading
    16.8 Exercises

17 Nature Language
    17.1 Signal Processing
    17.2 Syntax and Parsing
    17.3 Semantics and Meaning
    17.4 Pragmatics
    17.5 Natural Language Generation
    17.6 Further Reading
    17.7 Exercises

18 Expert Systems
    18.1 Examples and History
    18.2 Advantages of Expert Systems
    18.3 CYC and Other VLKB Projects
    18.4 AI as an Experimental Discipline
    18.5 Further Reading
    18.6 Exercises

19 Concluding Remarks
    19.1 Public Perception of AI
    19.2 Public Understanding of AI
    19.3 Applications of AI

Bibliography
Author Index
Subject Index

Details

No. of pages:
430
Language:
English
Copyright:
© Morgan Kaufmann 1993
Published:
Imprint:
Morgan Kaufmann
eBook ISBN:
9780323139687

About the Author

Matt Ginsberg