Probabilistic Approaches to Robotic Perception (Record no. 57768)

000 -LEADER
fixed length control field 03600nam a22005655i 4500
001 - CONTROL NUMBER
control field 978-3-319-02006-8
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200421112227.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 130830s2014 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783319020068
-- 978-3-319-02006-8
082 04 - CLASSIFICATION NUMBER
Call Number 629.892
100 1# - AUTHOR NAME
Author Ferreira, Jo�ao Filipe.
245 10 - TITLE STATEMENT
Title Probabilistic Approaches to Robotic Perception
300 ## - PHYSICAL DESCRIPTION
Number of Pages XXIX, 242 p.
490 1# - SERIES STATEMENT
Series statement Springer Tracts in Advanced Robotics,
520 ## - SUMMARY, ETC.
Summary, etc 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".
700 1# - AUTHOR 2
Author 2 Miranda Dias, Jorge.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-319-02006-8
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
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-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2014.
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-- txt
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-- computer
-- c
-- rdamedia
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-- online resource
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-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Image processing.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Robotics.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Automation.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Cognitive psychology.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Robotics and Automation.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence (incl. Robotics).
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Cognitive Psychology.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Image Processing and Computer Vision.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Signal, Image and Speech Processing.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 1610-7438 ;
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-- ZDB-2-ENG

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