View in EDS

COMPOSER: A Probabilistic Solution to the Utility Problem in Speed-up Learning.

Saved in:
Bibliographic Details
Title: COMPOSER: A Probabilistic Solution to the Utility Problem in Speed-up Learning.
Language: English
Authors: Gratch, Jonathan, DeJong, Gerald, Illinois Univ., Urbana. Dept. of Computer Science.
Peer Reviewed: N
Page Count: 17
Publication Date: 1992
Sponsoring Agency: National Science Foundation, Washington, DC.
Document Type: Information Analyses
Reports - Research
Descriptors: Algorithms, Artificial Intelligence, Comparative Analysis, Computer System Design, Learning Strategies, Planning, Probability, Problem Solving, Research Needs, Search Strategies, Statistical Analysis, Systems Development
Abstract: In machine learning there is considerable interest in techniques which improve planning ability. Initial investigations have identified a wide variety of techniques to address this issue. Progress has been hampered by the utility problem, a basic tradeoff between the benefit of learned knowledge and the cost to locate and apply relevant knowledge. In this paper we describe the COMPOSER system. COMPOSER embodies a probabilistic solution to the utility problem. It is implemented in the PRODIGY architecture. We compare COMPOSER to four other approaches which appear in the literature: (1) PRODIGY/EBL's Utility Analysis; (2) STATIC's Nonrecursive Hypothesis; (3) DYNAMIC: A Composite System; and (4) PALO's Chernoff Bounds. (Contains 24 references.) (Author/ALF)
Entry Date: 1993
Accession Number: ED353955
Database: ERIC
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED353955
    Name: ERIC Full Text
    Category: fullText
    Text: Full Text from ERIC
Header DbId: eric
DbLabel: ERIC
An: ED353955
AccessLevel: 3
PubType: Report
PubTypeId: report
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: COMPOSER: A Probabilistic Solution to the Utility Problem in Speed-up Learning.
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Gratch%2C+Jonathan%22">Gratch, Jonathan</searchLink><br /><searchLink fieldCode="AR" term="%22DeJong%2C+Gerald%22">DeJong, Gerald</searchLink><br /><searchLink fieldCode="AR" term="%22Illinois+Univ%2E%2C+Urbana%2E+Dept%2E+of+Computer+Science%2E%22">Illinois Univ., Urbana. Dept. of Computer Science.</searchLink>
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: N
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 17
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 1992
– Name: SourceSuprt
  Label: Sponsoring Agency
  Group: SrcSuprt
  Data: National Science Foundation, Washington, DC.
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Information Analyses<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+Analysis%22">Comparative Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+System+Design%22">Computer System Design</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Strategies%22">Learning Strategies</searchLink><br /><searchLink fieldCode="DE" term="%22Planning%22">Planning</searchLink><br /><searchLink fieldCode="DE" term="%22Probability%22">Probability</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+Solving%22">Problem Solving</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Needs%22">Research Needs</searchLink><br /><searchLink fieldCode="DE" term="%22Search+Strategies%22">Search Strategies</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+Analysis%22">Statistical Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+Development%22">Systems Development</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In machine learning there is considerable interest in techniques which improve planning ability. Initial investigations have identified a wide variety of techniques to address this issue. Progress has been hampered by the utility problem, a basic tradeoff between the benefit of learned knowledge and the cost to locate and apply relevant knowledge. In this paper we describe the COMPOSER system. COMPOSER embodies a probabilistic solution to the utility problem. It is implemented in the PRODIGY architecture. We compare COMPOSER to four other approaches which appear in the literature: (1) PRODIGY/EBL's Utility Analysis; (2) STATIC's Nonrecursive Hypothesis; (3) DYNAMIC: A Composite System; and (4) PALO's Chernoff Bounds. (Contains 24 references.) (Author/ALF)
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 1993
– Name: AN
  Label: Accession Number
  Group: ID
  Data: ED353955
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED353955
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
    Subjects:
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Comparative Analysis
        Type: general
      – SubjectFull: Computer System Design
        Type: general
      – SubjectFull: Learning Strategies
        Type: general
      – SubjectFull: Planning
        Type: general
      – SubjectFull: Probability
        Type: general
      – SubjectFull: Problem Solving
        Type: general
      – SubjectFull: Research Needs
        Type: general
      – SubjectFull: Search Strategies
        Type: general
      – SubjectFull: Statistical Analysis
        Type: general
      – SubjectFull: Systems Development
        Type: general
    Titles:
      – TitleFull: COMPOSER: A Probabilistic Solution to the Utility Problem in Speed-up Learning.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Illinois Univ., Urbana. Dept. of Computer Science.
      – PersonEntity:
          Name:
            NameFull: Gratch, Jonathan
      – PersonEntity:
          Name:
            NameFull: DeJong, Gerald
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 1992
ResultId 1