Robot Swarms Decide under Perception Errors in Best-of-N Problems
Robot swarms have been used extensively to examine best-of-N decisions; however, most studies presume that robots can reliably estimate the quality values of the various options. In an attempt to bridge the gap to reality, in this study, we assume robots with low-quality sensors take inaccurate measurements in both directions of overestimating and underestimating the quality of available options. We propose the use of three algorithms for allowing robots to identify themselves individually based on both their own measurements and the measurements of their dynamic neighborhood. Within the decision-making process, we then weigh the opinions of robots who define themselves as inaccurately lower than others. Our research compares the classification accuracy of the three algorithms and looks into the swarm’s decision accuracy when the best algorithm for classification is used.
Saved in:
| Main Author: | |
|---|---|
| Format: | Article/Letter to editor biblioteca |
| Language: | English |
| Subjects: | Best-of-N problem, Collective decision-making, Collective perception, Robot swarm, |
| Online Access: | https://research.wur.nl/en/publications/robot-swarms-decide-under-perception-errors-in-best-of-n-problems |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
dig-wur-nl-wurpubs-596299 |
|---|---|
| record_format |
koha |
| spelling |
dig-wur-nl-wurpubs-5962992025-01-14 Khaluf, Yara Article/Letter to editor Applied Sciences (Switzerland) 12 (2022) 6 ISSN: 2076-3417 Robot Swarms Decide under Perception Errors in Best-of-N Problems 2022 Robot swarms have been used extensively to examine best-of-N decisions; however, most studies presume that robots can reliably estimate the quality values of the various options. In an attempt to bridge the gap to reality, in this study, we assume robots with low-quality sensors take inaccurate measurements in both directions of overestimating and underestimating the quality of available options. We propose the use of three algorithms for allowing robots to identify themselves individually based on both their own measurements and the measurements of their dynamic neighborhood. Within the decision-making process, we then weigh the opinions of robots who define themselves as inaccurately lower than others. Our research compares the classification accuracy of the three algorithms and looks into the swarm’s decision accuracy when the best algorithm for classification is used. en application/pdf https://research.wur.nl/en/publications/robot-swarms-decide-under-perception-errors-in-best-of-n-problems 10.3390/app12062975 https://edepot.wur.nl/568165 Best-of-N problem Collective decision-making Collective perception Robot swarm https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ Wageningen University & Research |
| institution |
WUR NL |
| collection |
DSpace |
| country |
Países bajos |
| countrycode |
NL |
| component |
Bibliográfico |
| access |
En linea |
| databasecode |
dig-wur-nl |
| tag |
biblioteca |
| region |
Europa del Oeste |
| libraryname |
WUR Library Netherlands |
| language |
English |
| topic |
Best-of-N problem Collective decision-making Collective perception Robot swarm Best-of-N problem Collective decision-making Collective perception Robot swarm |
| spellingShingle |
Best-of-N problem Collective decision-making Collective perception Robot swarm Best-of-N problem Collective decision-making Collective perception Robot swarm Khaluf, Yara Robot Swarms Decide under Perception Errors in Best-of-N Problems |
| description |
Robot swarms have been used extensively to examine best-of-N decisions; however, most studies presume that robots can reliably estimate the quality values of the various options. In an attempt to bridge the gap to reality, in this study, we assume robots with low-quality sensors take inaccurate measurements in both directions of overestimating and underestimating the quality of available options. We propose the use of three algorithms for allowing robots to identify themselves individually based on both their own measurements and the measurements of their dynamic neighborhood. Within the decision-making process, we then weigh the opinions of robots who define themselves as inaccurately lower than others. Our research compares the classification accuracy of the three algorithms and looks into the swarm’s decision accuracy when the best algorithm for classification is used. |
| format |
Article/Letter to editor |
| topic_facet |
Best-of-N problem Collective decision-making Collective perception Robot swarm |
| author |
Khaluf, Yara |
| author_facet |
Khaluf, Yara |
| author_sort |
Khaluf, Yara |
| title |
Robot Swarms Decide under Perception Errors in Best-of-N Problems |
| title_short |
Robot Swarms Decide under Perception Errors in Best-of-N Problems |
| title_full |
Robot Swarms Decide under Perception Errors in Best-of-N Problems |
| title_fullStr |
Robot Swarms Decide under Perception Errors in Best-of-N Problems |
| title_full_unstemmed |
Robot Swarms Decide under Perception Errors in Best-of-N Problems |
| title_sort |
robot swarms decide under perception errors in best-of-n problems |
| url |
https://research.wur.nl/en/publications/robot-swarms-decide-under-perception-errors-in-best-of-n-problems |
| work_keys_str_mv |
AT khalufyara robotswarmsdecideunderperceptionerrorsinbestofnproblems |
| _version_ |
1822265588198670336 |