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010   $a3-319-53609-5
024 7 $a10.1007/978-3-319-53609-5
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035   $a(DE-He213)978-3-319-53609-5
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035   $a(OCoLC)973879035
035   $a(PPN)198869150
035   $a(EXLCZ)993710000001079865
100   $a20170215d2017      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aAchieving Consensus in Robot Swarms $eDesign and Analysis of Strategies for the best-of-n Problem /$fby Gabriele Valentini
205   $a1st ed. 2017.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017.
215   $a1 online resource (XIV, 146 p. 46 illus., 37 illus. in color.) 
225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v706
311   $a3-319-53608-7 
320   $aIncludes bibliographical references.
327   $aIntroduction -- Part 1:Background and Methodology -- Discrete Consensus Achievement in Arti?cial 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.
330   $aThis book focuses on the design and analysis of collective decision-making strategies for the best-of-n problem. After providing a formalization of the structure of the best-of-n problem supported by a comprehensive survey of the swarm robotics literature, it introduces the functioning of a collective decision-making strategy and identi?es a set of mechanisms that are essential for a strategy to solve the best-of-n problem. The best-of-n problem is an abstraction that captures the frequent requirement of a robot swarm to choose one option from of a ?nite set when optimizing bene?ts and costs. The book leverages the identi?cation of these mechanisms to develop a modular and model-driven methodology to design collective decision-making strategies and to analyze their performance at different level of abstractions. Lastly, the author provides a series of case studies in which the proposed methodology is used to design different strategies, using robot experiments to show how the designed strategies can be ported to different application scenarios.
410  0$aStudies in Computational Intelligence,$x1860-949X ;$v706
606   $aComputational intelligence
606   $aRobotics
606   $aAutomation
606   $aArtificial intelligence
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aRobotics and Automation$3https://scigraph.springernature.com/ontologies/product-market-codes/T19020
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
615  0$aComputational intelligence.
615  0$aRobotics.
615  0$aAutomation.
615  0$aArtificial intelligence.
615 14$aComputational Intelligence.
615 24$aRobotics and Automation.
615 24$aArtificial Intelligence.
676   $a006.3824
700   $aValentini$b Gabriele$4aut$4http://id.loc.gov/vocabulary/relators/aut$0911790
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910254162503321
996   $aAchieving Consensus in Robot Swarms$92041863
997   $aUNINA