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008			170227s2017 sz a ob 000 0 eng d
020			_a3319536095 _q(electronic bk.)
020			_a9783319536095 _q(electronic bk.)
020			_z3319536087
020			_z9783319536088 _q(print)
040			_aGW5XE _cGW5XE _dYDX _dOCLCF _dCOO _dIOG _dAZU _dUWO _dVT2 _dUPM _dMERER _dESU _dZ5A _dOCLCQ _dJBG _dIAD _dICW _dICN _dOTZ _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aQ337.3 _bV354 2017 EB
100	1		_aValentini, Gabriele, _eautor
245	1	0	_aAchieving consensus in robot swarms : _bdesign and analysis of strategies for the best-of-n problem _cGabriele Valentini.
264		1	_aCham, Switzerland _bSpringer _c2017.
300			_a1 recurso en línea (xiv, 146 páginas) _bilustraciones (algunas a color)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aStudies in computational intelligence _x1860-949X _vvolume 706
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas
505	0		_aIntroduction -- Part 1:Background and Methodology -- Discrete Consensus Achievement in Artificial Systems -- Modular Design of Strategies for the Best-of-n Problem -- Part 2:Mathematical Modeling and Analysis -- Indirect Modulation of Majority-Based Decisions -- Direct Modulation of Voter-Based Decisions -- Direct Modulation of Majority-Based Decisions -- Part 3:Robot Experiments -- A Robot Experiment in Site Selection -- A Robot Experiment in Collective Perception -- Part 4:Discussion and Annexes -- Conclusions -- Background on Markov Chains.
520	3		_aThis book focuses on the design and analysis of collective decision-making strategies for the best-of-n problem. After providing a formalization of the structure of the best-of-n problem supported by a comprehensive survey of the swarm robotics literature, it introduces the functioning of a collective decision-making strategy and identifies a set of mechanisms that are essential for a strategy to solve the best-of-n problem. The best-of-n problem is an abstraction that captures the frequent requirement of a robot swarm to choose one option from of a finite set when optimizing benefits and costs. The book leverages the identification of these mechanisms to develop a modular and model-driven methodology to design collective decision-making strategies and to analyze their performance at different level of abstractions. Lastly, the author provides a series of case studies in which the proposed methodology is used to design different strategies, using robot experiments to show how the designed strategies can be ported to different application scenarios.
650		7	_aRobótica _2embne _0(OCoLC)fst01098997 _0 _9160676
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-53609-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017C
998			_b02/2018 _dz _e- _zSI
999			_c95445 _d95445 _x1