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: Probabilistic Approaches to Robotic Perception
  name:
    name part: Ferreira, João Filipe.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Miranda Dias, Jorge.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2014
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXIX, 242 p. online resource.
  abstract: This book tries to address the following questions: How should the uncertainty and incompleteness inherent to sensing the environment be represented and modelled in a way that will increase the autonomy of a robot? How should a robotic system perceive, infer, decide and act efficiently? These are two of the challenging questions robotics community and robotic researchers have been facing. The development of robotic domain by the 1980s spurred the convergence of automation to autonomy, and the field of robotics has consequently converged towards the field of artificial intelligence (AI). Since the end of that decade, the general public’s imagination has been stimulated by high expectations on autonomy, where AI and robotics try to solve difficult cognitive problems through algorithms developed from either philosophical and anthropological conjectures or incomplete notions of cognitive reasoning. Many of these developments do not unveil even a few of the processes through which biological organisms solve these same problems with little energy and computing resources. The tangible results of this research tendency were many robotic devices demonstrating good performance, but only under well-defined and constrained environments. The adaptability to different and more complex scenarios was very limited. In this book, the application of Bayesian models and approaches are described in order to develop artificial cognitive systems that carry out complex tasks in real world environments, spurring the design of autonomous, intelligent and adaptive artificial systems, inherently dealing with uncertainty and the “irreducible incompleteness of models”.
  note: by João Filipe Ferreira, Jorge Miranda Dias.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Image processing
  subject:
    topic: Robotics
  subject:
    topic: Automation
  subject:
    topic: Cognitive psychology
  subject:
    topic: Engineering
  subject:
    topic: Robotics and Automation
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Cognitive Psychology
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Signal, Image and Speech Processing
  classification: TJ210.2-211.495
  classification: T59.5
  classification: 629.892
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Tracts in Advanced Robotics, 91
  identifier: 9783319020068
  identifier: http://dx.doi.org/10.1007/978-3-319-02006-8
  record info:
    record creation date: 130830
    record change date: 20151218132134.0
    record identifier: 978-3-319-02006-8