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Knowledge Acquisition from Structural Descriptions.

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Bibliographic Details
Title: Knowledge Acquisition from Structural Descriptions.
Language: English
Authors: Hayes-Roth, Frederick, McDermott, John, Rand Corp., Santa Monica, CA.
Availability: Publications Department, The Rand Corporation, 1700 Main Street, Santa Monica, California 90406 ($5.00)
Peer Reviewed: N
Page Count: 67
Publication Date: 1976
Report Number: P-59l0
Document Type: Reports - Research
Descriptors: Algorithms, Cognitive Processes, Componential Analysis, Computational Linguistics, Computer Science, Concept Formation, Difficulty Level, Induction, Models, Task Analysis, Teaching Machines
Geographic Terms: U.S.; California
Abstract: The learning machine described in this paper acquires concepts representable as conjunctive forms of the predicate calculus and behaviors representable as productions (antecedent-consequent pairs of such conjunctive forms): these concepts and behavior rules are inferred from sequentially presented pairs of examples by an algorithm that is probably effective for a wide variety of problems. A method for inducing knowledge by abstracting such representations from a sequence of training examples is described. The proposed learning method, interference matching, induces abstractions by finding regional properties common to two or more exemplars. Three tasks solved by a program that performs an interference matching algorithm are presented. Several problems concerning the relational representation of examples and the induction of knowledge by interference matching are also discussed. The similarities between this task and other computer science problems are indicated, and directions for future research are considered. (Author/JEG)
Journal Code: RIEMAY1979
Entry Date: 1979
Accession Number: ED163912
Database: ERIC
FullText Text:
  Availability: 0
Header DbId: eric
DbLabel: ERIC
An: ED163912
AccessLevel: 3
PubType: Report
PubTypeId: report
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Knowledge Acquisition from Structural Descriptions.
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Hayes-Roth%2C+Frederick%22">Hayes-Roth, Frederick</searchLink><br /><searchLink fieldCode="AR" term="%22McDermott%2C+John%22">McDermott, John</searchLink><br /><searchLink fieldCode="AR" term="%22Rand+Corp%2E%2C+Santa+Monica%2C+CA%2E%22">Rand Corp., Santa Monica, CA.</searchLink>
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Publications Department, The Rand Corporation, 1700 Main Street, Santa Monica, California 90406 ($5.00)
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: N
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 67
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 1976
– Name: NumberReport
  Label: Report Number
  Group: ID
  Data: P-59l0
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Processes%22">Cognitive Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Componential+Analysis%22">Componential Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+Linguistics%22">Computational Linguistics</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science%22">Computer Science</searchLink><br /><searchLink fieldCode="DE" term="%22Concept+Formation%22">Concept Formation</searchLink><br /><searchLink fieldCode="DE" term="%22Difficulty+Level%22">Difficulty Level</searchLink><br /><searchLink fieldCode="DE" term="%22Induction%22">Induction</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Task+Analysis%22">Task Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+Machines%22">Teaching Machines</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22U%2ES%2E%3B+California%22">U.S.; California</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The learning machine described in this paper acquires concepts representable as conjunctive forms of the predicate calculus and behaviors representable as productions (antecedent-consequent pairs of such conjunctive forms): these concepts and behavior rules are inferred from sequentially presented pairs of examples by an algorithm that is probably effective for a wide variety of problems. A method for inducing knowledge by abstracting such representations from a sequence of training examples is described. The proposed learning method, interference matching, induces abstractions by finding regional properties common to two or more exemplars. Three tasks solved by a program that performs an interference matching algorithm are presented. Several problems concerning the relational representation of examples and the induction of knowledge by interference matching are also discussed. The similarities between this task and other computer science problems are indicated, and directions for future research are considered. (Author/JEG)
– Name: CodeSource
  Label: Journal Code
  Group: SrcInfo
  Data: <searchLink fieldCode="JC" term="%22RIEMAY1979%22">RIEMAY1979</searchLink>
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 1979
– Name: AN
  Label: Accession Number
  Group: ID
  Data: ED163912
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED163912
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 67
    Subjects:
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Cognitive Processes
        Type: general
      – SubjectFull: Componential Analysis
        Type: general
      – SubjectFull: Computational Linguistics
        Type: general
      – SubjectFull: Computer Science
        Type: general
      – SubjectFull: Concept Formation
        Type: general
      – SubjectFull: Difficulty Level
        Type: general
      – SubjectFull: Induction
        Type: general
      – SubjectFull: Models
        Type: general
      – SubjectFull: Task Analysis
        Type: general
      – SubjectFull: Teaching Machines
        Type: general
      – SubjectFull: U.S.; California
        Type: general
    Titles:
      – TitleFull: Knowledge Acquisition from Structural Descriptions.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Rand Corp., Santa Monica, CA.
      – PersonEntity:
          Name:
            NameFull: Hayes-Roth, Frederick
      – PersonEntity:
          Name:
            NameFull: McDermott, John
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 11
              Type: published
              Y: 1976
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