Logical Foundations of Artificial Intelligence

Logical Foundations of Artificial Intelligence

1st Edition - July 1, 1987

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  • Authors: Michael Genesereth, Nils Nilsson
  • eBook ISBN: 9780128015544

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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



Product details

  • No. of pages: 406
  • Language: English
  • Copyright: © Morgan Kaufmann 1987
  • Published: July 1, 1987
  • Imprint: Morgan Kaufmann
  • eBook ISBN: 9780128015544

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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