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Logical Foundations of Artificial Intelligence - 1st Edition - ISBN: 9780934613316, 9780128015544

Logical Foundations of Artificial Intelligence

1st Edition

Authors: Michael Genesereth Nils Nilsson
eBook ISBN: 9780128015544
Imprint: Morgan Kaufmann
Published Date: 1st July 1987
Page Count: 406
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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


    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


    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


    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


    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


    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


    7 Induction
      7.1 Induction

      7.2 Concept Formation

      7.3 Experiment Generation

      7.4 Bibliographical and Historical Remarks


    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


    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


    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


    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


    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


    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


    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




No. of pages:
© Morgan Kaufmann 1987
1st July 1987
Morgan Kaufmann
eBook ISBN:

About the Authors

Michael Genesereth

Nils Nilsson

Nils 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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