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Achieving consensus in robot swarmsdesign and analysis of strategies for the best-of-n problem /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Achieving consensus in robot swarmsby Gabriele Valentini.
Reminder of title:	design and analysis of strategies for the best-of-n problem /
Author:	Valentini, Gabriele.
Published:	Cham :Springer International Publishing :2017.
Description:	xiv, 146 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Swarm intelligence.
Online resource:	http://dx.doi.org/10.1007/978-3-319-53609-5
ISBN:	9783319536095$q(electronic bk.)

Achieving consensus in robot swarmsdesign and analysis of strategies for the best-of-n problem /
Valentini, Gabriele.

Achieving consensus in robot swarmsdesign and analysis of strategies for the best-of-n problem /[electronic resource] :by Gabriele Valentini. - Cham :Springer International Publishing :2017. - xiv, 146 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.7061860-949X ;. - Studies in computational intelligence ;v. 216..

Introduction -- 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.

This 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.

ISBN: 9783319536095$q(electronic bk.)

Standard No.: 10.1007/978-3-319-53609-5doiSubjects--Topical Terms:

237730
Swarm intelligence.

LC Class. No.: Q337.3

Dewey Class. No.: 006.3824

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520 $a This 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.
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