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: Real-World Reasoning: Toward Scalable, Uncertain Spatiotemporal, Contextual and Causal Inference
  name:
    name part: Goertzel, Ben.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Geisweiller, Nil.
    role:
      role term: author.
  name:
    name part: Coelho, Lucio.
    role:
      role term: author.
  name:
    name part: Janičić, Predrag.
    role:
      role term: author.
  name:
    name part: Pennachin, Cassio.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: fr
    date issued: 2011
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: IX, 269 p. 59 illus., 1 illus. in color. online resource.
  abstract: The general problem addressed in this book is a large and important one: how to usefully deal with huge storehouses of complex information about real-world situations. Every one of the major modes of interacting with such storehouses – querying, data mining, data analysis – is addressed by current technologies only in very limited and unsatisfactory ways. The impact of a solution to this problem would be huge and pervasive, as the domains of human pursuit to which such storehouses are acutely relevant is numerous and rapidly growing. Finally, we give a more detailed treatment of one potential solution with this class, based on our prior work with the Probabilistic Logic Networks (PLN) formalism. We show how PLN can be used to carry out realworld reasoning, by means of a number of practical examples of reasoning regarding human activities inreal-world situations.
  table of contents: Introduction -- Knowledge Representation Using Formal Logic -- Quantifying and Managing Uncertainty -- Representing Temporal Knowledge -- Temporal Reasoning -- Representing and Reasoning On Spatial Knowledge -- Representing and Reasoning on Contextual Knowledge -- Causal Reasoning -- Extracting Logical Knowledge from Raw Data -- Scalable Spatiotemporal Logical Knowledge Storage -- Mining Patterns from Large Spatiotemporal Logical Knowledge Stores -- Probabilistic Logic Networks -- Temporal and Contextual Reasoning in PLN -- Inferring the Causes of Observed Changes.-Adaptive Inference Control.
  note: by Ben Goertzel, Nil Geisweiller, Lucio Coelho, Predrag Janičić, Cassio Pennachin.
  subject:
    topic: Computer science
  subject:
    topic: Artificial intelligence
  subject:
    topic: Management information systems
  subject:
    topic: Computer Science
  subject:
    topic: Computer Science, general
  subject:
    topic: Management of Computing and Information Systems
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  classification: QA75.5-76.95
  classification: 004
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Atlantis Thinking Machines, 1
  identifier: 9789491216114
  identifier: http://dx.doi.org/10.2991/978-94-91216-11-4
  record info:
    record creation date: 111201
    record change date: 20151125222152.0
    record identifier: 978-94-91216-11-4