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Logical Foundations of Artificial Intelligence

  • 1st Edition - July 1, 1987
  • Latest edition
  • Authors: Michael R. Genesereth, Nils J. Nilsson
  • Language: English

Intended both as a text for advanced undergraduates and graduate students, and as a key reference work for AI researchers and developers, Logical Foundations of Artificial Intel… Read more

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Description

Intended both as a text for advanced undergraduates and graduate students, and as a key reference work for AI researchers and developers, Logical Foundations of Artificial Intelligence is a lucid, rigorous, and comprehensive account of the fundamentals of artificial intelligence from the standpoint of logic.

The first section of the book introduces the logicist approach to AI--discussing the representation of declarative knowledge and featuring an introduction to the process of conceptualization, the syntax and semantics of predicate calculus, and the basics of other declarative representations such as frames and semantic nets. This section also provides a simple but powerful inference procedure, resolution, and shows how it can be used in a reasoning system.

The next several chapters discuss nonmonotonic reasoning, induction, and reasoning under uncertainty, broadening the logical approach to deal with the inadequacies of strict logical deduction. The third section introduces modal operators that facilitate representing and reasoning about knowledge. This section also develops the process of writing predicate calculus sentences to the metalevel--to permit sentences about sentences and about reasoning processes. The final three chapters discuss the representation of knowledge about states and actions, planning, and intelligent system architecture.

End-of-chapter bibliographic and historical comments provide background and point to other works of interest and research. Each chapter also contains numerous student exercises (with solutions provided in an appendix) to reinforce concepts and challenge the learner. A bibliography and index complete this comprehensive work.

Table of contents

Logical Foundations of Artificial Intelligence

by Michael R. Genesreth and Nils J. Nilsson

    Typographical Conventions

    1 Introduction
      1.1 Bibliographical and Historical Remarks

      Exercises


    2 Declarative Knowledge
      2.1 Conceptualization


      2.2 Predicate Calculus


      2.3 Semantics


      2.4 Blocks World Example


      2.5 Circuits Example


      2.6 Algebraic Examples


      2.7 List Examples


      2.8 Natural-Language Examples


      2.9 Specialized Languages


      2.10 Bibliographical and Historical Remarks

      Exercises


    3 Inference
      3.1 Derivability


      3.2 Inference Procedures


      3.3 Logical Implication


      3.4 Provability


      3.5 Proving Provability


      3.6 Bibliographical and Historical Remarks

      Exercises


    4 Resolution
      4.1 Clausal Form


      4.2 Unification


      4.3 Resolution Principle


      4.4 Resolution


      4.5 Unsatisfiability


      4.6 True-or-False Questions


      4.7 Fill-in-the-Blank Questions


      4.8 Circuits Example


      4.9 Mathematics Example


      4.10 Soundness and Completeness


      4.11 Resolution and Equality


      4.12 Bibliographical and Historical Remarks

      Exercises


    5 Resolution Strategies
      5.1 Deletion Strategies


      5.2 Unit Resolution


      5.3 Input Resolution


      5.4 Linear Resolution


      5.5 Set of Support Resolution


      5.6 Ordered Resolution


      5.7 Directed Resolution


      5.8 Sequential Constraint Satisfaction


      5.9 Bibliographical and Historical Remarks

      Exercises


    6 Nonmonotonic Reasoning
      6.1 The Closed-World Assumption


      6.2 Predicate Completion


      6.3 Taxonomic Hierarchies and Default Reasoning


      6.4 Circumscription


      6.5 More General Forms of Circumscription


      6.6 Default Theories


      6.7 Bibliographical and Historical Remarks

      Exercises


    7 Induction
      7.1 Induction


      7.2 Concept Formation


      7.3 Experiment Generation


      7.4 Bibliographical and Historical Remarks

      Exercises


    8 Reasoning with Uncertain Beliefs
      8.1 Probabilities of Sentences


      8.2 Using Bayes' Rule in Uncertain Reasoning


      8.3 Uncertain Reasoning in Expert Systems


      8.4 Probabilistic Logic


      8.5 Probabilistic Entailment


      8.6 Computations Appropriate for Small Matrices


      8.7 Dealing with Large Matrices


      8.8 Probabilities Conditioned on Specific Information


      8.9 Bibliographical and Historical Remarks

      Exercises


    9 Knowledge and Belief
      9.1 Preliminaries


      9.2 Sentential Logics of Belief


      9.3 Proof Methods


      9.4 Nested Beliefs


      9.5 Quantifying-In


      9.6 Proof Methods for Quantified Beliefs


      9.7 Knowing What Something Is


      9.8 Possible-Worlds Logics


      9.9 Properties of Knowledge


      9.10 Properties of Belief


      9.11 Group Knowledge


      9.12 Equality, Quantification, and Knowledge


      9.13 Bibliographical and Historical Remarks

      Exercises


    10 Metaknowledge and Metareasoning
      10.1 Metalanguage


      10.2 Clausal Form


      10.3 Resolution Principle


      10.4 Inference Procedures


      10.5 Derivability and Belief


      10.6 Metalevel Reasoning


      10.7 Bilevel Reasoning


      10.8 Reflection


      10.9 Bibliographical and Historical Remarks

      Exercises


    11 State and Change
      11.1 States


      11.2 Actions


      11.3 The Frame Problem


      11.4 Action Ordering


      11.5 Conditionality


      11.6 Bibliographical and Historical Remarks

      Exercises


    12 Planning
      12.1 Initial State


      12.2 Goals


      12.3 Actions


      12.4 Plans


      12.5 Green's Method


      12.6 Action Blocks


      12.7 Conditional Plans


      12.8 Planning Direction


      12.9 Unachievability Pruning


      12.10 State Alignment


      12.11 Frame-Axiom Suppression


      12.12 Goal Regression


      12.13 State Differences


      12.14 Bibliographical and Historical Remarks

      Exercises


    13 Intelligent-Agent Architecture
      13.1 Tropistic Agents


      13.2 Hysteretic Agents


      13.3 Knowledge-Level Agents


      13.4 Stepped Knowledge-Level Agents


      13.5 Fidelity


      13.6 Deliberate Agents


      13.7 Bibliographical and Historical Remarks

      Exercises


    Answers to Exercises
      A.1 Introduction

      A.2 Declarative Knowledge

      A.3 Inference

      A.4 Resolution

      A.5 Resolution Strategies

      A.6 Nonmonotonic Reasoning

      A.7 Induction

      A.8 Reasoning with Uncertain Beliefs

      A.9 Knowledge and Belief

      A.10 Metaknowledge and Metareasoning

      A.11 State and Change

      A.12 Planning

      A.13 Intelligent-Agent Architecture

    References

    Index

Product details

  • Edition: 1
  • Latest edition
  • Published: July 1, 1987
  • Language: English

About the author

NN

Nils J. Nilsson

Nils J. Nilsson's long and rich research career has contributed much to AI. He has written many books, including the classic Principles of Artificial Intelligence. Dr. Nilsson is Kumagai Professor of Engineering, Emeritus, at Stanford University. He has served on the editorial boards of Artificial Intelligence and Machine Learning and as an Area Editor for the Journal of the Association for Computing Machinery. Former Chairman of the Department of Computer Science at Stanford, and former Director of the SRI Artificial Intelligence Center, he is also a past president and Fellow of the American Association for Artificial Intelligence.
Affiliations and expertise
Stanford University

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