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Essentials of Artificial Intelligence

  • 1st Edition - April 1, 1993
  • Latest edition
  • Author: Matt Ginsberg
  • Language: English

Since its publication, Essentials of Artificial Intelligence has beenadopted at numerous universities and colleges offering introductory AIcourses at the graduate and undergrad… Read more

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Description

Since its publication, Essentials of Artificial Intelligence has beenadopted at numerous universities and colleges offering introductory AIcourses at the graduate and undergraduate levels. Based on the author'scourse at Stanford University, the book is an integrated, cohesiveintroduction to the field. The author has a fresh, entertaining writingstyle that combines clear presentations with humor and AI anecdotes. At thesame time, as an active AI researcher, he presents the materialauthoritatively and with insight that reflects a contemporary, first handunderstanding of the field. Pedagogically designed, this book offers arange of exercises and examples.

Table of contents

Essentials of Artificial Intelligenceby Matt Ginsberg
    Part I Introduction and Overview
      1 Introduction: What is AI?
        1.1 Defining Artificial Intelligence
          1.1.1 Intelligence1.1.2 Artifacts1.1.3 Construction
        1.2 What AI is About
          1.2.1 The Subfields of AI1.2.2 The Role of Examples in AI
        1.3 What AI Is Like1.4 Further Reading1.5 Exercises
      2 Overview
        2.1 Intelligent Action2.2 Search
          2.2.1 Blind Search2.2.2 Heuristic Search2.2.3 Other Issues2.2.4 Search: Examples
        2.3 Knowledge Representation
          2.3.1 Knowledge Representation: Examples
        2.4 Applications: Examples2.5 Further Reading2.6 Exercises
    Part II Search3 Blind Search
      3.1 Breadth-First Search3.2 Depth-First Search3.3 Iterative Deepening3.4 Iterative Broadening3.5 Searching Graphs
        3.5.1 Open and Closed Lists3.5.2 Dynamic Backtracking
      3.6 Further Reading3.7 Exercises
    4 Heuristic Search
      4.1 Search as Function Maximization
        4.1.1 Hill Climbing4.1.2 Simulated Annealing
      4.2 A*
        4.2.1 Admissibility4.2.2 Examples
      4.3 Extensions and IDA*4.4 Further Reading4.5 Exercises
    5 Adversary Search
      5.1 Assumptions5.2 Minimax
        5.2.1 Quiescence and Singular Extensions5.2.2 The Horizon Effect
      5.3 ((( Search5.4 Further Reading5.5 Exercises
Part III Knowledge Representation: Logic6 Introduction to Knowledge Representation
    6.1 A Programming Analogy6.2 Syntax6.3 Semantics6.4 Soundness and Completeness6.5 how Hard Is Theorem Proving?6.6 Further Reading6.7 Exercises
7 Predicate Logic
    7.1 Inference Using Modus Ponens7.2 Horn Databases7.3 The Resolution Rule7.4 Backward Chaining Using Resolution7.5 Normal Form7.6 Further Reading7.7 Exercises
8 First-Order Logic
    8.1 Databases with Quantifiers8.2 Unification8.3 Skolemizing Queries8.4 Finding the Most General Unifier8.5 Modus Ponens and Horn Databases8.6 Resolution and Normal Form8.7 Further Reading8.8 Exercises
9 Putting Logic to Work: Control of Reasoning
    9.1 Resolution Strategies9.2 Compile-Time and Run-Time Control9.3 The Role of Metalevel Reasoning in AI9.4 Runtime Control of Search
      9.4.1 Lookahead9.4.2 The Cheapest-First Heuristic9.4.3 Dependency-Directed Backtracking and Backjumping
    9.5 Declarative Control of Search9.6 Further Reading9.7 Exercises
Part IV Knowledge Representation: Other Techniques10 Assumption-Based Truth Maintenance
    10.1 Definition10.2 Applications
      10.2.1 Synthesis problems: Planning and Design10.2.2 Diagnosis10.2.3 Database Updates
    10.3 Implementation10.4 Further Reading10.5 Exercises
11 Nonmonotonic Reasoning
    11.1 Examples
      11.1.1 Inheritance Hierarchies11.1.2 The Frame Problem11.1.3 Diagnosis
    11.2 Definition
      11.2.1 Extensions11.2.2 Multiple Extensions
    11.3 Computational Problems11.4 Final Remarks11.5 Further Reading11.6 Exercises
12 Probability
    12.1 MYCIN and Certainty Factors12.2 Bayes' Rule and the Axioms of Probability12.3 Influence Diagrams12.4 Arguments For and Against Probability in AI12.5 Further Reading12.6 Exercises
13 Putting Knowledge to Work: Frames and Semantic Nets
    13.1 Introductory Examples
      13.1.1 Frames13.1.2 Semantic Nets
    13.2 Extensions
      13.2.1 Multiple Instances13.2.2 Nonunary Predicates
    13.3 Inference in Monotonic Frame Systems13.4 Inference in Nonmonotonic Frame Systems13.5 Further Reading13.6 Exercises
Part V AI Systems14 Planning
    14.1 General-Purpose and Special-Purpose Planners14.2 Reasoning about Action14.3 Descriptions of Action
      14.3.1 Nondeclarative Methods14.3.2 Monotonic Methods14.3.3 Nonmonotonic Methods
    14.4 Search in Planning
      14.4.1 Hierarchical Planning14.4.2 Subgoal Ordering and Nonlinear Planning14.4.3 Subgoal Interaction and the Sussman Anomaly
    14.5 Implementing a Planner14.6 Further Reading14.7 Exercises
15 Learning
    15.1 Discovery Learning15.2 Inductive Learning
      15.2.1 PAC Learning15.2.2 Version Spaces15.2.3 Neural Networks15.2.4 ID3
    15.3 Explanation-Based Learning15.4 Further Reading15.5 Exercises
16 Vision
    16.1 Digitization16.2 Low-Level Processing
      16.2.1 Noise Removal16.2.2 Feature Detection
    16.3 Segmentation and the Hough Transform16.4 Recovering 3-D Information
      16.4.1 The Waltz Algorithm16.4.2 The 2½-D Sketch
    16.5 Active Vision16.6 Object and Scene Recognition16.7 Further Reading16.8 Exercises
17 Nature Language
    17.1 Signal Processing17.2 Syntax and Parsing17.3 Semantics and Meaning17.4 Pragmatics17.5 Natural Language Generation17.6 Further Reading17.7 Exercises
18 Expert Systems
    18.1 Examples and History18.2 Advantages of Expert Systems18.3 CYC and Other VLKB Projects18.4 AI as an Experimental Discipline18.5 Further Reading18.6 Exercises
19 Concluding Remarks
    19.1 Public Perception of AI19.2 Public Understanding of AI19.3 Applications of AI
BibliographyAuthor IndexSubject Index

Product details

  • Edition: 1
  • Latest edition
  • Published: November 13, 2012
  • Language: English

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