| 000 | 03702nam a22005655i 4500 | ||
|---|---|---|---|
| 001 | 978-3-319-53609-5 | ||
| 003 | DE-He213 | ||
| 005 | 20220801215209.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 170215s2017 sz | s |||| 0|eng d | ||
| 020 |
_a9783319536095 _9978-3-319-53609-5 |
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| 024 | 7 |
_a10.1007/978-3-319-53609-5 _2doi |
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| 050 | 4 | _aQ342 | |
| 072 | 7 |
_aUYQ _2bicssc |
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_aTEC009000 _2bisacsh |
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| 072 | 7 |
_aUYQ _2thema |
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| 082 | 0 | 4 |
_a006.3 _223 |
| 100 | 1 |
_aValentini, Gabriele. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _942920 |
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| 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. |
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| 300 |
_aXIV, 146 p. 46 illus., 37 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aStudies in Computational Intelligence, _x1860-9503 ; _v706 |
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| 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. _97716 |
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| 650 | 0 |
_aControl engineering. _931970 |
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| 650 | 0 |
_aRobotics. _92393 |
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| 650 | 0 |
_aAutomation. _92392 |
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| 650 | 0 |
_aArtificial intelligence. _93407 |
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| 650 | 1 | 4 |
_aComputational Intelligence. _97716 |
| 650 | 2 | 4 |
_aControl, Robotics, Automation. _931971 |
| 650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
| 710 | 2 |
_aSpringerLink (Online service) _942921 |
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| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319536088 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319536101 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319851969 |
| 830 | 0 |
_aStudies in Computational Intelligence, _x1860-9503 ; _v706 _942922 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-53609-5 |
| 912 | _aZDB-2-ENG | ||
| 912 | _aZDB-2-SXE | ||
| 942 | _cEBK | ||
| 999 |
_c77218 _d77218 |
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