The benefits of interaction constraints in distributed autonomous systems

Michael Crosscombe; Jonathan Lawry

doi:10.1007/978-3-031-51497-5_2

The benefits of interaction constraints in distributed autonomous systems

Michael Crosscombe^*, Jonathan Lawry

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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Abstract

The design of distributed autonomous systems often omits consideration of the underlying network dynamics. Recent works in multi-agent systems and swarm robotics alike have highlighted the impact that the interactions between agents have on the collective behaviours exhibited by the system. In this paper, we seek to highlight the role that the underlying interaction network plays in determining the performance of the collective behaviour of a system, comparing its impact with that of the physical network. We contextualise this by defining a collective learning problem in which agents must reach a consensus about their environment in the presence of noisy information. We show that the physical connectivity of the agents plays a less important role than when an interaction network of limited connectivity is imposed on the system to constrain agent communication. Constraining agent interactions in this way drastically improves the performance of the system in a collective learning context. Additionally, we provide further evidence for the idea that ‘less is more’ when it comes to propagating information in distributed autonomous systems for the purpose of collective learning.

Original language	English
Title of host publication	Distributed Autonomous Robotic Systems.
Subtitle of host publication	DARS 2022
Editors	Julien Bourgeois, Benoît Piranda, Sabine Hauert, Heiko Hamann, Fumitoshi Matsuno, Abdallah Makhoul, Jamie Paik, Justin Werfel, Alyssa Pierson, Tin Lun Lam, Negar Mehr
Publisher	Springer, Cham
Pages	14-27
Number of pages	14
ISBN (Electronic)	9783031514975
ISBN (Print)	9783031514968
DOIs	https://doi.org/10.1007/978-3-031-51497-5_2
Publication status	Published - 1 Feb 2024
Event	The 16th International Symposium on Distributed Autonomous Robotic Systems 2022 - Montbeliard, France Duration: 28 Nov 2022 → 30 Nov 2022 Conference number: 16 https://dars2022.org/#/

Publication series

Name	Springer Proceedings in Advanced Robotics
Volume	28
ISSN (Print)	2511-1256
ISSN (Electronic)	2511-1264

Conference

Conference	The 16th International Symposium on Distributed Autonomous Robotic Systems 2022
Abbreviated title	DARS
Period	28/11/22 → 30/11/22
Internet address	https://dars2022.org/#/

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

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10.1007/978-3-031-51497-5_2

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Accepted author manuscript, 1.41 MBLicence: CC BY

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T-B PHASE: Prosperity Partnership with Thales
Richards, A. G. (Principal Investigator), Wilson, R. E. (Co-Investigator), Johnson, A. (Collaborator), Bullock, S. (Co-Investigator), Lawry, J. (Co-Investigator), Noyes, J. M. (Co-Investigator), Hauert, S. (Co-Investigator), Bode, N. W. F. (Co-Investigator), Pitonakova, L. (Researcher), Kent, T. (Researcher), Crosscombe, M. (Researcher), Zanatto, D. (Researcher), Alkan, B. (Researcher), Drury, K. L. (Manager), Hogg, E. (Student), Bonnell, W. D. (Student), Bennett, C. (Student), Clarke, C. E. M. (Student), Potts, M. W. (Student), Sartor, P. N. (Collaborator), Harvey, D. (Collaborator), Rayneau-Kirkhope, B. (Collaborator), Galvin, K. (Collaborator), Lam, J. (Collaborator), Barden, E. (Collaborator), Chattington, M. (Collaborator), Radanovic, M. (Researcher), Morey, E. J. (Student), Ball, M. (Co-Principal Investigator), Hunt, E. R. (Collaborator), Richards, A. G. (Principal Investigator), Radanovic, M. (Researcher), Morey, E. J. (Student), Steane, V. (Collaborator), Reed Edworthy, J. (Collaborator) & Hart, S. G. (Student)
1/10/17 → 31/03/23
Project: Research

Cite this

Crosscombe, M., & Lawry, J. (2024). The benefits of interaction constraints in distributed autonomous systems. In J. Bourgeois, B. Piranda, S. Hauert, H. Hamann, F. Matsuno, A. Makhoul, J. Paik, J. Werfel, A. Pierson, T. L. Lam, & N. Mehr (Eds.), Distributed Autonomous Robotic Systems.: DARS 2022 (pp. 14-27). (Springer Proceedings in Advanced Robotics; Vol. 28). Springer, Cham. https://doi.org/10.1007/978-3-031-51497-5_2

Crosscombe, Michael ; Lawry, Jonathan. / The benefits of interaction constraints in distributed autonomous systems. Distributed Autonomous Robotic Systems.: DARS 2022. editor / Julien Bourgeois ; Benoît Piranda ; Sabine Hauert ; Heiko Hamann ; Fumitoshi Matsuno ; Abdallah Makhoul ; Jamie Paik ; Justin Werfel ; Alyssa Pierson ; Tin Lun Lam ; Negar Mehr. Springer, Cham, 2024. pp. 14-27 (Springer Proceedings in Advanced Robotics).

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abstract = "The design of distributed autonomous systems often omits consideration of the underlying network dynamics. Recent works in multi-agent systems and swarm robotics alike have highlighted the impact that the interactions between agents have on the collective behaviours exhibited by the system. In this paper, we seek to highlight the role that the underlying interaction network plays in determining the performance of the collective behaviour of a system, comparing its impact with that of the physical network. We contextualise this by defining a collective learning problem in which agents must reach a consensus about their environment in the presence of noisy information. We show that the physical connectivity of the agents plays a less important role than when an interaction network of limited connectivity is imposed on the system to constrain agent communication. Constraining agent interactions in this way drastically improves the performance of the system in a collective learning context. Additionally, we provide further evidence for the idea that {\textquoteleft}less is more{\textquoteright} when it comes to propagating information in distributed autonomous systems for the purpose of collective learning.",

author = "Michael Crosscombe and Jonathan Lawry",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; The 16th International Symposium on<br/>Distributed Autonomous Robotic Systems 2022, DARS ; Conference date: 28-11-2022 Through 30-11-2022",

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Crosscombe, M & Lawry, J 2024, The benefits of interaction constraints in distributed autonomous systems. in J Bourgeois, B Piranda, S Hauert, H Hamann, F Matsuno, A Makhoul, J Paik, J Werfel, A Pierson, TL Lam & N Mehr (eds), Distributed Autonomous Robotic Systems.: DARS 2022. Springer Proceedings in Advanced Robotics, vol. 28, Springer, Cham, pp. 14-27, The 16th International Symposium on
Distributed Autonomous Robotic Systems 2022, 28/11/22. https://doi.org/10.1007/978-3-031-51497-5_2

The benefits of interaction constraints in distributed autonomous systems. / Crosscombe, Michael; Lawry, Jonathan.
Distributed Autonomous Robotic Systems.: DARS 2022. ed. / Julien Bourgeois; Benoît Piranda; Sabine Hauert; Heiko Hamann; Fumitoshi Matsuno; Abdallah Makhoul; Jamie Paik; Justin Werfel; Alyssa Pierson; Tin Lun Lam; Negar Mehr. Springer, Cham, 2024. p. 14-27 (Springer Proceedings in Advanced Robotics; Vol. 28).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - The benefits of interaction constraints in distributed autonomous systems

AU - Crosscombe, Michael

AU - Lawry, Jonathan

N1 - Conference code: 16

PY - 2024/2/1

Y1 - 2024/2/1

N2 - The design of distributed autonomous systems often omits consideration of the underlying network dynamics. Recent works in multi-agent systems and swarm robotics alike have highlighted the impact that the interactions between agents have on the collective behaviours exhibited by the system. In this paper, we seek to highlight the role that the underlying interaction network plays in determining the performance of the collective behaviour of a system, comparing its impact with that of the physical network. We contextualise this by defining a collective learning problem in which agents must reach a consensus about their environment in the presence of noisy information. We show that the physical connectivity of the agents plays a less important role than when an interaction network of limited connectivity is imposed on the system to constrain agent communication. Constraining agent interactions in this way drastically improves the performance of the system in a collective learning context. Additionally, we provide further evidence for the idea that ‘less is more’ when it comes to propagating information in distributed autonomous systems for the purpose of collective learning.

AB - The design of distributed autonomous systems often omits consideration of the underlying network dynamics. Recent works in multi-agent systems and swarm robotics alike have highlighted the impact that the interactions between agents have on the collective behaviours exhibited by the system. In this paper, we seek to highlight the role that the underlying interaction network plays in determining the performance of the collective behaviour of a system, comparing its impact with that of the physical network. We contextualise this by defining a collective learning problem in which agents must reach a consensus about their environment in the presence of noisy information. We show that the physical connectivity of the agents plays a less important role than when an interaction network of limited connectivity is imposed on the system to constrain agent communication. Constraining agent interactions in this way drastically improves the performance of the system in a collective learning context. Additionally, we provide further evidence for the idea that ‘less is more’ when it comes to propagating information in distributed autonomous systems for the purpose of collective learning.

U2 - 10.1007/978-3-031-51497-5_2

DO - 10.1007/978-3-031-51497-5_2

M3 - Conference Contribution (Conference Proceeding)

SN - 9783031514968

T3 - Springer Proceedings in Advanced Robotics

SP - 14

EP - 27

BT - Distributed Autonomous Robotic Systems.

A2 - Bourgeois, Julien

A2 - Piranda, Benoît

A2 - Hauert, Sabine

A2 - Hamann, Heiko

A2 - Matsuno, Fumitoshi

A2 - Makhoul, Abdallah

A2 - Paik, Jamie

A2 - Werfel, Justin

A2 - Pierson, Alyssa

A2 - Lam, Tin Lun

A2 - Mehr, Negar

PB - Springer, Cham

T2 - The 16th International Symposium on<br/>Distributed Autonomous Robotic Systems 2022

Y2 - 28 November 2022 through 30 November 2022

ER -

Crosscombe M, Lawry J. The benefits of interaction constraints in distributed autonomous systems. In Bourgeois J, Piranda B, Hauert S, Hamann H, Matsuno F, Makhoul A, Paik J, Werfel J, Pierson A, Lam TL, Mehr N, editors, Distributed Autonomous Robotic Systems.: DARS 2022. Springer, Cham. 2024. p. 14-27. (Springer Proceedings in Advanced Robotics). doi: 10.1007/978-3-031-51497-5_2

The benefits of interaction constraints in distributed autonomous systems

Abstract

Publication series

Conference

Bibliographical note

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Projects

T-B PHASE: Prosperity Partnership with Thales

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