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03369nam a22004935i 4500 |
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978-3-319-53609-5 |
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DE-He213 |
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20170215163611.0 |
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170215s2017 gw | s |||| 0|eng d |
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|a 9783319536095
|9 978-3-319-53609-5
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|a 10.1007/978-3-319-53609-5
|2 doi
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|a COM004000
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|a Valentini, Gabriele.
|e author.
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|a Achieving Consensus in Robot Swarms
|h [electronic resource] :
|b Design and Analysis of Strategies for the best-of-n Problem /
|c by Gabriele Valentini.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2017.
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| 300 |
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|a XIV, 146 p. 46 illus., 37 illus. in color.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
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|a text file
|b PDF
|2 rda
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 706
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|a 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.
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|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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| 650 |
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|a Engineering.
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| 650 |
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|a Artificial intelligence.
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| 650 |
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|a Computational intelligence.
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| 650 |
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|a Robotics.
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| 650 |
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|a Automation.
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|a Engineering.
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| 650 |
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|a Computational Intelligence.
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| 650 |
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|a Robotics and Automation.
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| 650 |
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|a Artificial Intelligence (incl. Robotics).
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| 710 |
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|a SpringerLink (Online service)
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|t Springer eBooks
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| 776 |
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8 |
|i Printed edition:
|z 9783319536088
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| 830 |
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 706
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| 856 |
4 |
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|u http://dx.doi.org/10.1007/978-3-319-53609-5
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-ENG
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| 950 |
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|a Engineering (Springer-11647)
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