Using Reinforcement Learning in Partial Order Plan Space Metadata

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Title

  • Main Title Using Reinforcement Learning in Partial Order Plan Space

Creator

  • Author: Ceylan, Hakan
    Creator Type: Personal

Contributor

  • Chair: Swigger, Kathleen M.
    Contributor Type: Personal
    Contributor Info: Major Professor
  • Committee Member: Brazile, Robert
    Contributor Type: Personal
  • Committee Member: Mihalcea, Rada, 1974-
    Contributor Type: Personal

Publisher

  • Name: University of North Texas
    Place of Publication: Denton, Texas

Date

  • Creation: 2006-05
  • Digitized: 2008-04-22

Language

  • English

Description

  • Content Description: Partial order planning is an important approach that solves planning problems without completely specifying the orderings between the actions in the plan. This property provides greater flexibility in executing plans; hence making the partial order planners a preferred choice over other planning methodologies. However, in order to find partially ordered plans, partial order planners perform a search in plan space rather than in space of world states and an uninformed search in plan space leads to poor efficiency. In this thesis, I discuss applying a reinforcement learning method, called First-visit Monte Carlo method, to partial order planning in order to design agents which do not need any training data or heuristics but are still able to make informed decisions in plan space based on experience. Communicating effectively with the agent is crucial in reinforcement learning. I address how this task was accomplished in plan space and the results from an evaluation of a blocks world test bed.

Subject

  • Library of Congress Subject Headings: Reinforcement learning (Machine learning)
  • Library of Congress Subject Headings: Intelligent agents (Computer software)
  • Library of Congress Subject Headings: Artificial intelligence.
  • Keyword: artificial intelligence
  • Keyword: reinforcement learning
  • Keyword: partial order planning

Collection

  • Name: UNT Theses and Dissertations
    Code: UNTETD

Institution

  • Name: UNT Libraries
    Code: UNT

Rights

  • Rights Access: unt
  • Rights License: copyright
  • Rights Holder: Ceylan, Hakan
  • Rights Statement: Copyright is held by the author, unless otherwise noted. All rights reserved.

Resource Type

  • Thesis or Dissertation

Format

  • Text

Identifier

  • OCLC: 70890296
  • Archival Resource Key: ark:/67531/metadc5232

Degree

  • Degree Name: Master of Science
  • Degree Level: Master's
  • Degree Discipline: Computer Science
  • Academic Department: Department of Computer Science and Engineering
  • Degree Grantor: University of North Texas

Note

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