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An Analysis of Learning To Plan as a Search Problem.

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Title: An Analysis of Learning To Plan as a Search Problem.
Language: English
Authors: Gratch, Jonathan, DeJong, Gerald, Illinois Univ., Urbana. Dept. of Computer Science.
Peer Reviewed: N
Page Count: 19
Publication Date: 1992
Sponsoring Agency: National Science Foundation, Washington, DC.
Document Type: Information Analyses
Reports - Research
Descriptors: Artificial Intelligence, Computer System Design, Evaluation Methods, Learning Strategies, Planning, Problem Solving, Search Strategies, Systems Development
Abstract: Increasingly, machine learning is entertained as a mechanism for improving the efficiency of planning systems. Research in this area has generated an impressive battery of techniques and a growing body of empirical successes. Unfortunately the formal properties of these systems are not well understood. This is highlighted by a growing corpus of demonstrations where learning actually degrades planning performance. In this paper we view learning to plan as a search problem. Learning is seen as a transformational process where a planner is tailored to a particular domain and problem distribution. To accomplish this task, learning systems draw from a vocabulary of transformation operators such as macro-operators or control rules. These "learning operators" define a space of possible transformations through which a system must search for an efficient planner. This study shows that the complexity of this search precludes a general solution and can only be approached via simplifications. (Frequently unarticulated commitments which underlie current learning approaches are illustrated.) These simplifications improve learning efficiency but not without tradeoffs. In some cases these tradeoffs result in less than optimal behavior. In others, they produce planners which become worse through learning. It is hoped that by articulating these commitments we can better understand their ramifications can be better understood. Finally, a particular learning technique--COMPOSER--is discussed which explicitly utilizes these simplifications to ensure performance improvements with reasonable efficiency. (Contains 34 references.) (Author/ALF)
Entry Date: 1993
Accession Number: ED353954
Database: ERIC
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  Data: An Analysis of Learning To Plan as a Search Problem.
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  Data: 19
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  Data: 1992
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  Data: National Science Foundation, Washington, DC.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+System+Design%22">Computer System Design</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+Methods%22">Evaluation Methods</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="%22Problem+Solving%22">Problem Solving</searchLink><br /><searchLink fieldCode="DE" term="%22Search+Strategies%22">Search Strategies</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+Development%22">Systems Development</searchLink>
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  Data: Increasingly, machine learning is entertained as a mechanism for improving the efficiency of planning systems. Research in this area has generated an impressive battery of techniques and a growing body of empirical successes. Unfortunately the formal properties of these systems are not well understood. This is highlighted by a growing corpus of demonstrations where learning actually degrades planning performance. In this paper we view learning to plan as a search problem. Learning is seen as a transformational process where a planner is tailored to a particular domain and problem distribution. To accomplish this task, learning systems draw from a vocabulary of transformation operators such as macro-operators or control rules. These "learning operators" define a space of possible transformations through which a system must search for an efficient planner. This study shows that the complexity of this search precludes a general solution and can only be approached via simplifications. (Frequently unarticulated commitments which underlie current learning approaches are illustrated.) These simplifications improve learning efficiency but not without tradeoffs. In some cases these tradeoffs result in less than optimal behavior. In others, they produce planners which become worse through learning. It is hoped that by articulating these commitments we can better understand their ramifications can be better understood. Finally, a particular learning technique--COMPOSER--is discussed which explicitly utilizes these simplifications to ensure performance improvements with reasonable efficiency. (Contains 34 references.) (Author/ALF)
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      – Text: English
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        PageCount: 19
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Computer System Design
        Type: general
      – SubjectFull: Evaluation Methods
        Type: general
      – SubjectFull: Learning Strategies
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      – SubjectFull: Planning
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      – SubjectFull: Problem Solving
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      – SubjectFull: Search Strategies
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      – SubjectFull: Systems Development
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      – TitleFull: An Analysis of Learning To Plan as a Search Problem.
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            NameFull: DeJong, Gerald
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              Type: published
              Y: 1992
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