---
_id: '60899'
abstract:
- lang: eng
text: "AbstractSocial insects such as ants and
termites communicate via pheromones which allow them to coordinate their activity
and solve complex tasks as a swarm, e.g. foraging for food or finding their way
back to the nest. This behavior was shaped through evolutionary processes over
millions of years. In computational models, self-coordination in swarms has been
implemented using probabilistic or pre-defined simple action rules to shape the
decision of each agent and the collective behavior. However, manual tuned decision
rules may limit the emergent behavior of the swarm. In this work we investigate
the emergence of self-coordination and communication in evolved swarms without
defining any explicit rule. For this purpose, we evolve a swarm of agents representing
an ant colony. We use an evolutionary algorithm to optimize a spiking neural network
(SNN) which serves as an artificial brain to control the behavior of each agent.
The goal of the evolved colony is to find optimal ways to forage for food and
return it to the nest in the shortest amount of time. In the evolutionary phase,
the ants are able to learn to collaborate by depositing pheromone near food piles
and near the nest to guide other ants. The pheromone usage is not manually encoded
into the network; instead, this behavior is established through the optimization
procedure. We observe that pheromone-based communication enables the ants to perform
better in comparison to colonies where communication via pheromone did not emerge.
Furthermore, we assess the foraging performance of the ant colonies by comparing
the SNN-based model to a multi-agent rule-based system. Our results show that
the SNN-based model can efficiently complete the foraging task in a short amount
of time. Our approach illustrates that even in the absence of pre-defined rules,
self-coordination via pheromone emerges as a result of the network optimization.
This work serves as a proof of concept for the possibility of creating complex
applications utilizing SNNs as underlying architectures for multi-agent interactions
where communication and self-coordination is desired.\r\n"
author:
- first_name: Cristian
full_name: Jimenez Romero, Cristian
last_name: Jimenez Romero
- first_name: Alper
full_name: Yegenoglu, Alper
id: '117951'
last_name: Yegenoglu
orcid: 0000-0001-8869-215X
- first_name: Aarón
full_name: Pérez Martín, Aarón
last_name: Pérez Martín
- first_name: Sandra
full_name: Diaz-Pier, Sandra
last_name: Diaz-Pier
- first_name: Abigail
full_name: Morrison, Abigail
last_name: Morrison
citation:
ama: Jimenez Romero C, Yegenoglu A, Pérez Martín A, Diaz-Pier S, Morrison A. Emergent
communication enhances foraging behavior in evolved swarms controlled by spiking
neural networks. Swarm Intelligence. 2023;18(1):1-29. doi:10.1007/s11721-023-00231-6
apa: Jimenez Romero, C., Yegenoglu, A., Pérez Martín, A., Diaz-Pier, S., & Morrison,
A. (2023). Emergent communication enhances foraging behavior in evolved swarms
controlled by spiking neural networks. Swarm Intelligence, 18(1),
1–29. https://doi.org/10.1007/s11721-023-00231-6
bibtex: '@article{Jimenez Romero_Yegenoglu_Pérez Martín_Diaz-Pier_Morrison_2023,
title={Emergent communication enhances foraging behavior in evolved swarms controlled
by spiking neural networks}, volume={18}, DOI={10.1007/s11721-023-00231-6},
number={1}, journal={Swarm Intelligence}, publisher={Springer Science and Business
Media LLC}, author={Jimenez Romero, Cristian and Yegenoglu, Alper and Pérez Martín,
Aarón and Diaz-Pier, Sandra and Morrison, Abigail}, year={2023}, pages={1–29}
}'
chicago: 'Jimenez Romero, Cristian, Alper Yegenoglu, Aarón Pérez Martín, Sandra
Diaz-Pier, and Abigail Morrison. “Emergent Communication Enhances Foraging Behavior
in Evolved Swarms Controlled by Spiking Neural Networks.” Swarm Intelligence
18, no. 1 (2023): 1–29. https://doi.org/10.1007/s11721-023-00231-6.'
ieee: 'C. Jimenez Romero, A. Yegenoglu, A. Pérez Martín, S. Diaz-Pier, and A. Morrison,
“Emergent communication enhances foraging behavior in evolved swarms controlled
by spiking neural networks,” Swarm Intelligence, vol. 18, no. 1, pp. 1–29,
2023, doi: 10.1007/s11721-023-00231-6.'
mla: Jimenez Romero, Cristian, et al. “Emergent Communication Enhances Foraging
Behavior in Evolved Swarms Controlled by Spiking Neural Networks.” Swarm Intelligence,
vol. 18, no. 1, Springer Science and Business Media LLC, 2023, pp. 1–29, doi:10.1007/s11721-023-00231-6.
short: C. Jimenez Romero, A. Yegenoglu, A. Pérez Martín, S. Diaz-Pier, A. Morrison,
Swarm Intelligence 18 (2023) 1–29.
date_created: 2025-08-06T15:02:25Z
date_updated: 2025-08-08T11:41:28Z
doi: 10.1007/s11721-023-00231-6
intvolume: ' 18'
issue: '1'
language:
- iso: eng
page: 1-29
publication: Swarm Intelligence
publication_identifier:
issn:
- 1935-3812
- 1935-3820
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: Emergent communication enhances foraging behavior in evolved swarms controlled
by spiking neural networks
type: journal_article
user_id: '117951'
volume: 18
year: '2023'
...
---
_id: '19992'
author:
- first_name: Gabriele
full_name: Valentini, Gabriele
last_name: Valentini
- first_name: Heiko
full_name: Hamann, Heiko
last_name: Hamann
citation:
ama: 'Valentini G, Hamann H. Time-variant feedback processes in collective decision-making
systems: influence and effect of dynamic neighborhood sizes. Swarm Intelligence.
2015:153-176. doi:10.1007/s11721-015-0108-8'
apa: 'Valentini, G., & Hamann, H. (2015). Time-variant feedback processes in
collective decision-making systems: influence and effect of dynamic neighborhood
sizes. Swarm Intelligence, 153–176. https://doi.org/10.1007/s11721-015-0108-8'
bibtex: '@article{Valentini_Hamann_2015, title={Time-variant feedback processes
in collective decision-making systems: influence and effect of dynamic neighborhood
sizes}, DOI={10.1007/s11721-015-0108-8},
journal={Swarm Intelligence}, author={Valentini, Gabriele and Hamann, Heiko},
year={2015}, pages={153–176} }'
chicago: 'Valentini, Gabriele, and Heiko Hamann. “Time-Variant Feedback Processes
in Collective Decision-Making Systems: Influence and Effect of Dynamic Neighborhood
Sizes.” Swarm Intelligence, 2015, 153–76. https://doi.org/10.1007/s11721-015-0108-8.'
ieee: 'G. Valentini and H. Hamann, “Time-variant feedback processes in collective
decision-making systems: influence and effect of dynamic neighborhood sizes,”
Swarm Intelligence, pp. 153–176, 2015.'
mla: 'Valentini, Gabriele, and Heiko Hamann. “Time-Variant Feedback Processes in
Collective Decision-Making Systems: Influence and Effect of Dynamic Neighborhood
Sizes.” Swarm Intelligence, 2015, pp. 153–76, doi:10.1007/s11721-015-0108-8.'
short: G. Valentini, H. Hamann, Swarm Intelligence (2015) 153–176.
date_created: 2020-10-13T09:27:48Z
date_updated: 2022-01-06T06:54:17Z
department:
- _id: '63'
- _id: '238'
doi: 10.1007/s11721-015-0108-8
language:
- iso: eng
page: 153-176
publication: Swarm Intelligence
publication_identifier:
issn:
- 1935-3812
- 1935-3820
publication_status: published
status: public
title: 'Time-variant feedback processes in collective decision-making systems: influence
and effect of dynamic neighborhood sizes'
type: journal_article
user_id: '15415'
year: '2015'
...
---
_id: '20369'
abstract:
- lang: eng
text: Designing and analyzing self-organizing systems such as robotic swarms is
a challenging task even though we have complete knowledge about the robot’s interior.
It is difficult to determine the individual robot’s behavior based on the swarm
behavior and vice versa due to the high number of agent–agent interactions. A
step towards a solution of this problem is the development of appropriate models
which accurately predict the swarm behavior based on a specified control algorithm.
Such models would reduce the necessary number of time-consuming simulations and
experiments during the design process of an algorithm. In this paper we propose
a model with focus on an explicit representation of space because the effectiveness
of many swarm robotic scenarios depends on spatial inhomogeneity. We use methods
of statistical physics to address spatiality. Starting from a description of a
single robot we derive an abstract model of swarm motion. The model is then extended
to a generic model framework of communicating robots. In two examples we validate
models against simulation results. Our experience shows that qualitative correctness
is easily achieved, while quantitative correctness is disproportionately more
difficult but still possible.
author:
- first_name: Heiko
full_name: Hamann, Heiko
last_name: Hamann
- first_name: Heinz
full_name: Wörn, Heinz
last_name: Wörn
citation:
ama: Hamann H, Wörn H. A framework of space–time continuous models for algorithm
design in swarm robotics. Swarm Intelligence. 2008;2(2-4):209-239. doi:10.1007/s11721-008-0015-3
apa: Hamann, H., & Wörn, H. (2008). A framework of space–time continuous models
for algorithm design in swarm robotics. Swarm Intelligence, 2(2–4),
209–239. https://doi.org/10.1007/s11721-008-0015-3
bibtex: '@article{Hamann_Wörn_2008, title={A framework of space–time continuous
models for algorithm design in swarm robotics}, volume={2}, DOI={10.1007/s11721-008-0015-3},
number={2–4}, journal={Swarm Intelligence}, author={Hamann, Heiko and Wörn, Heinz},
year={2008}, pages={209–239} }'
chicago: 'Hamann, Heiko, and Heinz Wörn. “A Framework of Space–Time Continuous Models
for Algorithm Design in Swarm Robotics.” Swarm Intelligence 2, no. 2–4
(2008): 209–39. https://doi.org/10.1007/s11721-008-0015-3.'
ieee: H. Hamann and H. Wörn, “A framework of space–time continuous models for algorithm
design in swarm robotics,” Swarm Intelligence, vol. 2, no. 2–4, pp. 209–239,
2008.
mla: Hamann, Heiko, and Heinz Wörn. “A Framework of Space–Time Continuous Models
for Algorithm Design in Swarm Robotics.” Swarm Intelligence, vol. 2, no.
2–4, 2008, pp. 209–39, doi:10.1007/s11721-008-0015-3.
short: H. Hamann, H. Wörn, Swarm Intelligence 2 (2008) 209–239.
date_created: 2020-11-16T14:05:49Z
date_updated: 2022-01-06T06:54:26Z
department:
- _id: '63'
- _id: '238'
doi: 10.1007/s11721-008-0015-3
intvolume: ' 2'
issue: 2-4
language:
- iso: eng
page: 209-239
publication: Swarm Intelligence
publication_identifier:
issn:
- 1935-3812
- 1935-3820
publication_status: published
status: public
title: A framework of space–time continuous models for algorithm design in swarm robotics
type: journal_article
user_id: '15415'
volume: 2
year: '2008'
...