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000779752 019__ $$a974470614$$a974546249$$a981851162
000779752 020__ $$a9783319536095$$q(electronic book)
000779752 020__ $$a3319536095$$q(electronic book)
000779752 020__ $$z9783319536088
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000779752 0247_ $$a10.1007/978-3-319-53609-5$$2doi
000779752 035__ $$aSP(OCoLC)ocn973879035
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000779752 050_4 $$aQ337.3
000779752 08204 $$a006.3/824$$223
000779752 1001_ $$aValentini, Gabriele,$$eauthor.
000779752 24510 $$aAchieving consensus in robot swarms :$$bdesign and analysis of strategies for the best-of-n problem /$$cGabriele Valentini.
000779752 264_1 $$aCham, Switzerland :$$bSpringer,$$c2017.
000779752 300__ $$a1 online resource (xiv, 146 pages) :$$billustrations.
000779752 336__ $$atext$$btxt$$2rdacontent
000779752 337__ $$acomputer$$bc$$2rdamedia
000779752 338__ $$aonline resource$$bcr$$2rdacarrier
000779752 347__ $$atext file$$bPDF$$2rda
000779752 4901_ $$aStudies in computational intelligence,$$x1860-949X ;$$vvolume 706
000779752 504__ $$aIncludes bibliographical references.
000779752 5050_ $$aIntroduction -- Part 1:Background and Methodology -- Discrete Consensus Achievement in Artificial Systems -- Modular Design of Strategies for the Best-of-n Problem -- Part 2:Mathematical Modeling and Analysis -- Indirect Modulation of Majority-Based Decisions -- Direct Modulation of Voter-Based Decisions -- Direct Modulation of Majority-Based Decisions -- Part 3:Robot Experiments -- A Robot Experiment in Site Selection -- A Robot Experiment in Collective Perception -- Part 4:Discussion and Annexes -- Conclusions -- Background on Markov Chains.
000779752 506__ $$aAccess limited to authorized users.
000779752 520__ $$aThis book focuses on the design and analysis of collective decision-making strategies for the best-of-n problem. After providing a formalization of the structure of the best-of-n problem supported by a comprehensive survey of the swarm robotics literature, it introduces the functioning of a collective decision-making strategy and identifies a set of mechanisms that are essential for a strategy to solve the best-of-n problem. The best-of-n problem is an abstraction that captures the frequent requirement of a robot swarm to choose one option from of a finite set when optimizing benefits and costs. The book leverages the identification of these mechanisms to develop a modular and model-driven methodology to design collective decision-making strategies and to analyze their performance at different level of abstractions. Lastly, the author provides a series of case studies in which the proposed methodology is used to design different strategies, using robot experiments to show how the designed strategies can be ported to different application scenarios.
000779752 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 27, 2017).
000779752 650_0 $$aSwarm intelligence.
000779752 650_0 $$aRobotics.
000779752 77608 $$iPrint version:$$z3319536087$$z9783319536088$$w(OCoLC)968663431
000779752 830_0 $$aStudies in computational intelligence ;$$vv. 706.
000779752 852__ $$bebk
000779752 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-53609-5$$zOnline Access$$91397441.1
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000779752 980__ $$aBIB
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