Skip to main content
SpringerLink
Log in

Dynamic Response Thresholds: Heterogeneous Ranges Allow Specialization While Mitigating Convergence to Sink States

  • Conference paper
  • First Online:
Swarm Intelligence (ANTS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12421))

Included in the following conference series:

Abstract

We argue that heterogeneous threshold ranges allow agents in a decentralized swarm to effectively adapt thresholds in response to dynamic task demands while avoiding the pitfalls of positive feedback sinks. Dynamic response thresholds allow agents to dynamically evolve specializations which can improve the responsiveness and stability of a swarm. Dynamic thresholds that adapt in response to previous experience, however, are vulnerable to getting stuck in sink states due to the positive feedback nature of such systems. We show that heterogeneous threshold ranges result in comparable task allocation and improved stability as compared to homogeneous threshold ranges, and that simple static random thresholds should be considered in situations where agent resources are plentiful.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    External factors include but are not limited to task stimuli and observed actions of other agents.

References

  1. Bonabeau, E., Theraulaz, G., Deneubourg, J.L.: Quantitative study of the fixed threshold model for the regulation of division of labor in insect societies. Proc. Royal Soc. London Biol. Sci. 263(1376), 1565–1569 (1996)

    Article  Google Scholar 

  2. Bonabeau, E., Theraulaz, G., Deneubourg, J.L.: Fixed response thresholds and the regulation of division of labor in insect societies. Bull. Math. Biol. 60, 753–807 (1998). https://doi.org/10.1006/bulm.1998.0041

    Article  MATH  Google Scholar 

  3. Brutschy, A., Pini, G., Pinciroli, C., Birattari, M., Dorigo, M.: Self-organized task allocation to sequentially interdependent tasks in swarm robots. Auton. Agent. Multi-Agent Syst. 25, 101–125 (2014)

    Article  Google Scholar 

  4. Brutschy, A., et al.: Costs and benefits of behavioral specialization. Robot. Auton. Syst. 60, 1408–1420 (2012)

    Article  Google Scholar 

  5. Campos, M., Bonabeau, E., Theraulaz, G., Deneubourg, J.: Dynamic scheduling and division of labor in social insects. Adapt. Behav. 8, 83–96 (2000)

    Article  Google Scholar 

  6. Castello, E., et al.: Adaptive foraging for simulated and real robotic swarms: the dynamical response threshold approach. Swarm Intell. 10, 1–31 (2018). https://doi.org/10.1007/s11721-015-0117-7

    Article  Google Scholar 

  7. Castello, E., Yamamoto, T., Nakamura, Y., Ishiguro, H.: Task allocation for a robotic swarm based on an adaptive response threshold model. In: Proceedings of the 13th IEEE International Conference on Control, Automation, and Systems, pp. 259–266 (2013)

    Google Scholar 

  8. Cicirello, V.A., Smith, S.F.: Distributed coordination of resources via wasp-like agents. In: Truszkowski, W., Hinchey, M., Rouff, C. (eds.) WRAC 2002. LNCS (LNAI), vol. 2564, pp. 71–80. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45173-0_5

    Chapter  Google Scholar 

  9. de Lope, J., Maravall, D., Quinonez, Y.: Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems. Robot. Auton. Syst. 61, 714–720 (2013)

    Article  Google Scholar 

  10. de Lope, J., Maravall, D., Quinonez, Y.: Self-organizing techniques to improve the decentralized multi-task distribution in multi-robot systems. Neurocomputing 163, 47–55 (2015)

    Article  Google Scholar 

  11. dos Santos, F., Bazzan, A.L.C.: An ant based algorithm for task allocation in large-scale and dynamic multiagent scenarios. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 73–80 (2009)

    Google Scholar 

  12. Gautrais, J., Theraulaz, G., Deneubourg, J., Anderson, C.: Emergent polyethism as a consequence of increase colony size in insect societies. J. Theoret. Biol. 215, 363–373 (2002)

    Article  Google Scholar 

  13. Goldingay, H., van Mourik, J.: The effect of load on agent-based algorithms for distributed task allocation. Inf. Sci. 222, 66–80 (2013)

    Article  Google Scholar 

  14. Jones, C., Mataric, M.J.: Adaptive division of labor in large-scale minimalist multi-robot systems. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1969–1974 (2003)

    Google Scholar 

  15. Jones, J.C., Myerscough, M.R., Graham, S., Oldroyd, B.P.: Honey bee nest thermoregulation: diversity promotes stability. Science 305(5682), 402–404 (2004)

    Article  Google Scholar 

  16. Kanakia, A., Touri, B., Correll, N.: Modeling multi-robot task allocation with limited information as global game. Swarm Intell. 10(2), 147–160 (2016). https://doi.org/10.1007/s11721-016-0123-4

    Article  Google Scholar 

  17. Kazakova, V.A., Wu, A.S.: Specialization vs. re-specialization: effects of Hebbian learning in a dynamic environment. In: Proceedings of the 31st International Florida Artificial Intelligence Research Society Conference, pp. 354–359 (2018)

    Google Scholar 

  18. Kazakova, V.A., Wu, A.S., Sukthankar, G.R.: Respecializing swarms by forgetting reinforced thresholds. Swarm Intell. 14(3), 171–204 (2020). https://doi.org/10.1007/s11721-020-00181-3

    Article  Google Scholar 

  19. Krieger, M.J.B., Billeter, J.B.: The call of duty: self-organised task allocation in a population of up to twelve mobile robots. Robot. Auton. Syst. 30, 65–84 (2000)

    Article  Google Scholar 

  20. Labella, T.H., Dorigo, M., Deneubourg, J.: Division of labor in a group of robots inspired by ants’ foraging behavior. ACM Trans. Auton. Adapt. Syst. 1(1), 4–25 (2006)

    Article  Google Scholar 

  21. Langridge, E.A., Franks, N.R., Sendova-Franks, A.B.: Improvement in collective performance with experience in ants. Behav. Ecol. Sociobiol. 56, 523–529 (2004). https://doi.org/10.1007/s00265-004-0824-3

    Article  Google Scholar 

  22. Lee, W., Kim, D.E.: Local interaction of agents for division of labor in multi-agent systems. In: Tuci, E., Giagkos, A., Wilson, M., Hallam, J. (eds.) SAB 2016. LNCS (LNAI), vol. 9825, pp. 46–54. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43488-9_5

    Chapter  Google Scholar 

  23. Lee, W., Kim, D.: History-based response threshold model for division of labor in multi-agent systems. Sensors 17, 1232 (2017)

    Article  Google Scholar 

  24. Lerman, K., Jones, C., Galstyan, A., Mataric, M.J.: Analysis of dynamic task allocation in multi-robot systems. Int. J. Robot. Res. 25, 225–241 (2006)

    Article  Google Scholar 

  25. Liu, W., Winfield, A., Sa, J., Chen, J., Dou, L.: Strategies for energy optimisation in a swarm of foraging robots. In: Şahin, E., Spears, W.M., Winfield, A.F.T. (eds.) SR 2006. LNCS, vol. 4433, pp. 14–26. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71541-2_2

    Chapter  Google Scholar 

  26. Liu, W., Winfield, A., Sa, J., Chen, J., Dou, L.: Towards energy optimisation: Emergent task allocation in a swarm of foraging robots. Adapt. Behav. 15, 289–305 (2007)

    Article  Google Scholar 

  27. Meyer, B., Weidenmuller, A., Chen, R., Garcia, J.: Collective homeostasis and time-resolved models of self-organised task allocation. In: Proceedings of the 9th EIA International Conference on Bio-inspired Information and Communication Technologies, pp. 469–478 (2015)

    Google Scholar 

  28. Price, R., Tiňo, P.: Evaluation of adaptive nature inspired task allocation against alternate decentralised multiagent strategies. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 982–990. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30217-9_99

    Chapter  Google Scholar 

  29. Ravary, F., Lecoutey, E., Kaminski, G., Chaline, N., Jaisson, P.: Individual experience alone can generate lasting division of labor in ants. Curr. Biol. 17, 1308–1312 (2007)

    Article  Google Scholar 

  30. Theraulaz, G., Bonabeau, E., Deneubourg, J.: Response threshold reinforcement and division of labour in insect societies. Proc. Royal Soc. B 265, 327–332 (1998)

    Article  Google Scholar 

  31. Weidenmüller, A.: The control of nest climate in bumblebee (\({B}ombus\)\(terrestris\)) colonies: interindividual variability and self reinforcement in fanning response. Behav. Ecol. 15, 120–128 (2004)

    Article  Google Scholar 

  32. Weidenmüller, A., Chen, R., Meyer, B.: Reconsidering response threshold models—short-term response patterns in thermoregulating bumblebees. Behav. Ecol. Sociobiol. 73(8), 1–13 (2019). https://doi.org/10.1007/s00265-019-2709-5

    Article  Google Scholar 

  33. Wu, A.S., Mathias, H.D., Giordano, J.P., Hevia, A.: Effects of response threshold distribution on dynamic division of labor in decentralized swarms. In: Proceedings of the 33rd International Florida Artificial Intelligence Research Society Conference (2020)

    Google Scholar 

  34. Wu, A.S., Riggs, C.: Inter-agent variation improves dynamic decentralized task allocation. In: Proceedings 31st International Florida Artificial Intelligence Research Society Conference, pp. 366–369 (2018)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Science Foundation under Grant No. IIS1816777.

Author information

Authors and Affiliations

  1. University of Central Florida, Orlando, FL, USA

    Annie S. Wu

  2. University of Wisconsin – La Crosse, La Crosse, WI, USA

    H. David Mathias

Authors
  1. Annie S. Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. David Mathias
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Annie S. Wu .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. Université Libre de Bruxelles, Brussels, Belgium

    Thomas Stützle

  3. Universitat Politècnica de Catalunya, Barcelona, Spain

    Maria J. Blesa

  4. Artificial Intelligence Research Institute, Bellaterra, Spain

    Christian Blum

  5. University of Lübeck, Lübeck, Germany

    Heiko Hamann

  6. Université Libre de Bruxelles, Brussels, Belgium

    Mary Katherine Heinrich

  7. Université Libre de Bruxelles, Brussels, Belgium

    Volker Strobel

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wu, A.S., Mathias, H.D. (2020). Dynamic Response Thresholds: Heterogeneous Ranges Allow Specialization While Mitigating Convergence to Sink States. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2020. Lecture Notes in Computer Science(), vol 12421. Springer, Cham. https://doi.org/10.1007/978-3-030-60376-2_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-60376-2_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60375-5

  • Online ISBN: 978-3-030-60376-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation