000 04299nam a22006015i 4500
001 978-3-319-53609-5
003 DE-He213
005 20210118131309.0
007 cr nn 008mamaa
008 170215s2017 gw | s |||| 0|eng d
020 _a9783319536095
_9978-3-319-53609-5
024 7 _a10.1007/978-3-319-53609-5
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aValentini, Gabriele.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aAchieving Consensus in Robot Swarms
_h[electronic resource] :
_bDesign and Analysis of Strategies for the best-of-n Problem /
_cby Gabriele Valentini.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXIV, 146 p. 46 illus., 37 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v706
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 _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 0 _aComputational intelligence.
650 0 _aRobotics.
650 0 _aAutomation.
650 0 _aArtificial intelligence.
650 1 4 _aComputational Intelligence.
_0http://scigraph.springernature.com/things/product-market-codes/T11014
650 2 4 _aRobotics and Automation.
_0http://scigraph.springernature.com/things/product-market-codes/T19020
650 2 4 _aArtificial Intelligence.
_0http://scigraph.springernature.com/things/product-market-codes/I21000
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319536088
776 0 8 _iPrinted edition:
_z9783319536101
776 0 8 _iPrinted edition:
_z9783319851969
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v706
856 4 0 _uhttps://doi.org/10.1007/978-3-319-53609-5
912 _aZDB-2-ENG
999 _c447747
_d447747
942 _cEB
506 _aAvailable to subscribing member institutions only. Доступно лише організаціям членам підписки.
506 _fOnline access from local network of NaUOA.
506 _fOnline access with authorization at https://link.springer.com/
506 _fОнлайн-доступ з локальної мережі НаУОА.
506 _fОнлайн доступ з авторизацією на https://link.springer.com/