ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2022
Social learning in swarm robotics.
Article in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Signalling and social learning in swarms of robots.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Research on Division of Labor Decision and System Stability of Swarm Robots Based on Mutual Information.Sensors (Basel, Switzerland) · 2024Article
- Nature-Inspired Intelligent Computing: A Comprehensive Survey.Research (Washington, D.C.) · 2024Review
- Smart self-propelled particles: a framework to investigate the cognitive bases of movement.Journal of the Royal Society, Interface · 2023Article
- Recent trends in robot learning and evolution for swarm robotics.Frontiers in robotics and AI · 2023Review
- Adaptivity: a path towards general swarm intelligence?Frontiers in robotics and AI · 2023Article
- From real-time adaptation to social learning in robot ecosystems.Frontiers in robotics and AI · 2023Article
- The emergence of collective knowledge and cumulative culture in animals, humans and machines.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Article
- Experiments in artificial culture: from noisy imitation to storytelling robots.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Article
- Artificial evolution of robot bodies and control: on the interaction between evolution, learning and culture.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Article
- Collective minds: social network topology shapes collective cognition.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we present an implementation of social learning for swarm robotics. We consider social learning as a distributed online reinforcement learning method applied to a collective of robots where sensing, acting and coordination are performed on a local basis. While some issues are specific to artificial systems, such as the general objective of learning efficient (and ideally, optimal) behavioural strategies to fulfill a task defined by a supervisor, some other issues are shared with social learning in natural systems. We discuss some of these issues, paving the way towards cumulative cultural evolution in robot swarms, which could enable complex social organization necessary to achieve challenging robotic tasks. This article is part of a discussion meeting issue 'The emergence of collective knowledge and cumulative culture in animals, humans and machines'.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.