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A framework for neurosymbolic robot action planning using large language models

Overview of attention for article published in Frontiers in Neurorobotics, June 2024
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (62nd percentile)

Mentioned by

twitter
7 X users

Readers on

mendeley
3 Mendeley
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Title
A framework for neurosymbolic robot action planning using large language models
Published in
Frontiers in Neurorobotics, June 2024
DOI 10.3389/fnbot.2024.1342786
Pubmed ID
Authors

Alessio Capitanelli, Fulvio Mastrogiovanni

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Postgraduate 1 33%
Unknown 2 67%
Readers by discipline Count As %
Engineering 1 33%
Unknown 2 67%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 June 2024.
All research outputs
#15,394,349
of 26,106,397 outputs
Outputs from Frontiers in Neurorobotics
#283
of 1,061 outputs
Outputs of similar age
#60,680
of 165,384 outputs
Outputs of similar age from Frontiers in Neurorobotics
#1
of 4 outputs
Altmetric has tracked 26,106,397 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,061 research outputs from this source. They receive a mean Attention Score of 4.1. This one has gotten more attention than average, scoring higher than 71% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 165,384 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them