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000695350 020__ $$a9783319020068 $$qelectronic book
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000695350 0247_ $$a10.1007/978-3-319-02006-8$$2doi
000695350 035__ $$aSP(OCoLC)ocn857900445
000695350 035__ $$aSP(OCoLC)857900445
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000695350 049__ $$aISEA
000695350 050_4 $$aTJ211.3
000695350 08204 $$a629.892$$223
000695350 1001_ $$aFerreira, João Filipe,$$eauthor.
000695350 24510 $$aProbabilistic approaches to robotic perception$$h[electronic resource]  /$$cJoão Filipe Ferreira, Jorge Miranda Dias.
000695350 264_1 $$aCham :$$bSpringer,$$c[2013?]
000695350 264_4 $$c©2014
000695350 300__ $$a1 online resource (xii, 234 pages).
000695350 336__ $$atext$$btxt$$2rdacontent
000695350 337__ $$acomputer$$bc$$2rdamedia
000695350 338__ $$aonline resource$$bcr$$2rdacarrier
000695350 4901_ $$aSpringer Tracts in Advanced Robotics,$$x1610-7438 ;$$v91
000695350 504__ $$aIncludes bibliographical references and index.
000695350 506__ $$aAccess limited to authorized users.
000695350 520__ $$aThis book tries to address the following questions: How should the uncertainty and incompleteness inherent to sensing the environment be represented and modelled in a way that will increase the autonomy of a robot? How should a robotic system perceive, infer, decide and act efficiently? These are two of the challenging questions robotics community and robotic researchers have been facing. The development of robotic domain by the 1980s spurred the convergence of automation to autonomy, and the field of robotics has consequently converged towards the field of artificial intelligence (AI). Since the end of that decade, the general public s imagination has been stimulated by high expectations on autonomy, where AI and robotics try to solve difficult cognitive problems through algorithms developed from either philosophical and anthropological conjectures or incomplete notions of cognitive reasoning. Many of these developments do not unveil even a few of the processes through which biological organisms solve these same problems with little energy and computing resources. The tangible results of this research tendency were many robotic devices demonstrating good performance, but only under well-defined and constrained environments. The adaptability to different and more complex scenarios was very limited. In this book, the application of Bayesian models and approaches are described in order to develop artificial cognitive systems that carry out complex tasks in real world environments, spurring the design of autonomous, intelligent and adaptive artificial systems, inherently dealing with uncertainty and the irreducible incompleteness of models.
000695350 588__ $$aDescription based on online resource; title from PDF title page (SpringerLink, viewed September 3, 2013).
000695350 650_0 $$aRobot vision.
000695350 7001_ $$aMiranda Dias, Jorge,$$eauthor.
000695350 830_0 $$aSpringer tracts in advanced robotics ;$$v91.
000695350 85280 $$bebk$$hSpringerLink
000695350 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-3-319-02006-8$$zOnline Access
000695350 909CO $$ooai:library.usi.edu:695350$$pGLOBAL_SET
000695350 980__ $$aEBOOK
000695350 980__ $$aBIB
000695350 982__ $$aEbook
000695350 983__ $$aOnline
000695350 994__ $$a92$$bISE