In vivo, in silico, in machina: Ants and Robots Balance Memory and Communication to Collectively Exploit Information

  • Melanie E. Moses
  • Kenneth Letendre
  • Joshua P. Hecker
  • Tatiana P. Flanagan
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Ants balance the use of remembered private information and communicated public information to maximally exploit resources. This work determines how the strategy that best balances these two sources of information, and the performance of that best strategy, depend on the information in the distribution that is available to be exploited, and the number of ants in the colony. We answer this question by (1) measuring the rates at which ants foraging for seeds in manipulative field studies, (2) simulating ant foraging strategies and measuring resulting foraging performance, and (3) implementing foraging strategies as algorithms for search behaviors in teams of cooperatively searching robots.

Keywords

Ants Swarm robotics Agent based modeling Evolutionary algorithms 

References

  1. 1.
    Beverly BD, McLendon H, Nacu S, Holmes S, Gordon DM (2009) How site fidelity leads to individual differences in the foraging activity of harvester ants. Behav Ecol 20(3):633–638 CrossRefGoogle Scholar
  2. 2.
    Crist TO, MacMahon JA (1991) Individual foraging components of harvester ants: movement patterns and seed patch fidelity. Insectes Soc 38(4):379–396 CrossRefGoogle Scholar
  3. 3.
    Dall SRX, Giraldeau L, Olsson O, McNamara JM, Stephens DW (2005) Information and its use by animals in evolutionary ecology. Trends Ecol Evol 20(4):187–193 CrossRefGoogle Scholar
  4. 4.
    Dorigo M, Sahin E (2004) Swarm robotics—special issue editorial. Auton Robots 17(2–3):111–113 CrossRefGoogle Scholar
  5. 5.
    Dorigo MV et al. (2004) Evolving self-organizing behaviors for a swarm-bot. Auton Robots 17(2):223–245 CrossRefGoogle Scholar
  6. 6.
    Flanagan TP, Letendre K, Burnside W, Fricke GM, Moses M (2011) How ants turn information into food. In: IEEE symposium on artificial life (ALIFE), pp 178–185 CrossRefGoogle Scholar
  7. 7.
    Flanagan TP, Letendre K, Moses ME (2012) Quantifying the effect of colony size and food distribution on harvester ant foraging. PLoS ONE 7(7):e39427 ADSCrossRefGoogle Scholar
  8. 8.
    Haefner JW, Crist TO (1994) Spatial model of movement and foraging in harvester ants (Pogonomyrmex) (I): the roles of memory and communication. J Theor Biol 166:299–313 CrossRefGoogle Scholar
  9. 9.
    Hecker JP, Letendre K, Stolleis K, Washington D, Moses ME (2012) Formica ex machina: ant swarm foraging from physical to virtual and back again. In: Proceedings of the 8th international conference on swarm intelligence, Brussels. Lecture Notes in Computer Science, vol 7461 Google Scholar
  10. 10.
    Holldobler B (1976) Recruitment behavior, home range orientation and territoriality in harvester ants, Pogonomyrmex. Behav Ecol Sociobiol 1(1):3–44 CrossRefGoogle Scholar
  11. 11.
    Letendre K, Moses ME (2012, in review) Synergy in ant foraging strategies: memory and communication alone and in combination. Unpublished Google Scholar
  12. 12.
    Banavar JR, Moses ME, Brown JH, Damuth J, Rinaldo A, Sibly RM, Maritan A (2010) A general basis for quarter power scaling in animals. Proc Natl Acad Sci USA 107(36):15816–158120 ADSCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Melanie E. Moses
    • 1
  • Kenneth Letendre
    • 1
  • Joshua P. Hecker
    • 1
  • Tatiana P. Flanagan
    • 1
  1. 1.Department of Computer ScienceUniversity of New MexicoAlbuquerqueUSA

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