rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Qualitative Spatial Abstraction in Reinforcement Learning
  name:
    name part: Frommberger, Lutz.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2010
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVII, 174 p. online resource.
  abstract: Reinforcement learning has developed as a successful learning approach for domains that are not fully understood and that are too complex to be described in closed form. However, reinforcement learning does not scale well to large and continuous problems. Furthermore, acquired knowledge specific to the learned task, and transfer of knowledge to new tasks is crucial. In this book the author investigates whether deficiencies of reinforcement learning can be overcome by suitable abstraction methods. He discusses various forms of spatial abstraction, in particular qualitative abstraction, a form of representing knowledge that has been thoroughly investigated and successfully applied in spatial cognition research. With his approach, he exploits spatial structures and structural similarity to support the learning process by abstracting from less important features and stressing the essential ones. The author demonstrates his learning approach and the transferability of knowledge by having his system learn in a virtual robot simulation system and consequently transfer the acquired knowledge to a physical robot. The approach is influenced by findings from cognitive science. The book is suitable for researchers working in artificial intelligence, in particular knowledge representation, learning, spatial cognition, and robotics. .
  table of contents: Foundations of Reinforcement Learning -- Abstraction and Knowledge Transfer in Reinforcement Learning -- Qualitative State Space Abstraction -- Generalization and Transfer Learning with Qualitative Spatial Abstraction -- RLPR – An Aspectualizable State Space Representation -- Empirical Evaluation -- Summary and Outlook.
  note: by Lutz Frommberger.
  subject:
    topic: Computer science
  subject:
    topic: Artificial intelligence
  subject:
    topic: Control engineering
  subject:
    topic: Robotics
  subject:
    topic: Mechatronics
  subject:
    topic: Computer Science
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Control, Robotics, Mechatronics
  classification: Q334-342
  classification: TJ210.2-211.495
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Cognitive Technologies
  identifier: 9783642165900
  identifier: http://dx.doi.org/10.1007/978-3-642-16590-0
  record info:
    record creation date: 101212
    record change date: 20151204170508.0
    record identifier: 978-3-642-16590-0