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001 978-3-319-02006-8
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008 130830s2014 gw | s |||| 0|eng d
020 _a9783319020068
_9978-3-319-02006-8
024 7 _a10.1007/978-3-319-02006-8
_2doi
050 4 _aTJ210.2-211.495
050 4 _aT59.5
072 7 _aTJFM1
_2bicssc
072 7 _aTEC037000
_2bisacsh
072 7 _aTEC004000
_2bisacsh
082 0 4 _a629.892
_223
100 1 _aFerreira, Jo�ao Filipe.
_eauthor.
245 1 0 _aProbabilistic Approaches to Robotic Perception
_h[electronic resource] /
_cby Jo�ao Filipe Ferreira, Jorge Miranda Dias.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXXIX, 242 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Tracts in Advanced Robotics,
_x1610-7438 ;
_v91
520 _aThis 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".
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aImage processing.
650 0 _aRobotics.
650 0 _aAutomation.
650 0 _aCognitive psychology.
650 1 4 _aEngineering.
650 2 4 _aRobotics and Automation.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aCognitive Psychology.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aSignal, Image and Speech Processing.
700 1 _aMiranda Dias, Jorge.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319020051
830 0 _aSpringer Tracts in Advanced Robotics,
_x1610-7438 ;
_v91
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-02006-8
912 _aZDB-2-ENG
942 _cEBK
999 _c57768
_d57768