Abstract
Cooperative behavior of social insects is widely studied and mimicked in Artificial Intelligence communities. One such interesting cooperation is observed in the form of philanthropic activity e.g. army ants build bridges using their own bodies along the route from a food source to the nest. Such altruistic behavior helps to optimize the food gathering performance of the ant colony. This paper presents a multi-agent simulation inspired by army ant behavior. Such cooperation in a multi agent system can be very valuable for engineering applications. The purpose of this study is to model and comprehend this biological behavior by computer simulation.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lioni, A., Sauwens, C., Theraulaz, G., Deneubourg, J.-L.: Chain formation in oecophylla longinoda. Journal of Insect Behavior 14(5), 679–696 (2001)
Lioni, A., Deneubourg, J.-L.: Collective decision through self-assembling. Naturwissenschaften 91(5), 237–241 (2004)
Azzag, H., Monmarche, N., Slimane, M., Venturini, G.: Anttree: a new model for clustering with artificial ants. In: IEEE Congress on Evolutionary Computation, vol. 4, pp. 2642–2647 (2003)
Anderson, C., Theraulaz, G., Deneubourg, J.L.: Self-assemblages in insect societies. Insectes Sociaux 49(2), 99–110 (2002)
Teodorovic, D.: Swarm intelligence systems for transportation engineering:principles and applications. Transportation research Part C: Emerging Technologies 16(6), 651–667 (2008)
Deneubourg, J.L., Lioni, A., Detrain, C.: Dynamics of aggregation and emergence of cooperation. The Biological Bulletin 202(3), 262–267 (2002)
Dorigo, M., Caro, G.D., Gambardella, L.M.: Ant algorithms for discreate optimization. Artifical Life 5(2), 137–172 (1999)
Iwasa, Y., Higashi, M., Yamamura, N.: Prey distribution as a factor determining the choice of optimal foraging strategy. The American Naturalist 117(5), 710–723 (1981)
Metivier, M., Lattaud, C., Heudin, J.-C.: A stress-based speciation model in lifedrop. In: Artificial life VIII: Proceedings of the Eighth International Conference on Artificial Life, pp. 121–126 (2003)
Payton, D., Estkowski, R., Howard, M.: Compound behaviors in pheromone robotics. Robotics and Autonomous Systems 44, 229–240 (2003)
Pollack, J.B., Lipson, H.: The golem project: Evolving hardware bodies and brains. In: The Second NASA/DoD Workshop on Evolvable Hardware, EH 2000 (2000)
Purnamadjaja, A.H., Russell, R.A.: Guiding robots’ behaviors using pheromone communication. Auton. Robots 23(2), 113–130 (2007)
Powell, S., Franks, N.R.: How a few help all: living pothole plugs speed prey delivery in the army ant Eciton burchellii. Animal Behaviour 73(6), 1067–1076 (2007)
von Mammen, S., Christian, J.: Evolutionary swarm design of architectural idea models. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 143–150. ACM, New York (2008)
Holldobler, B., Wilson, E.O.: The multiple recruitment system of the african weaver ant oecophylla longinoda. Behavioral Ecology and Sociobiology 3(1), 19–60 (1978)
Yamaguchi, M., Yoshimoto, E., Kondo, S.: Pattern regulation in the stripe of zebrafish suggests an underlying dynamic and autonomous mechanism. Proc. Natl. Acad. Sci. USA 104(12), 4790–4793 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ishiwata, H., Noman, N., Iba, H. (2010). Emergence of Cooperation in a Bio-inspired Multi-agent System. In: Li, J. (eds) AI 2010: Advances in Artificial Intelligence. AI 2010. Lecture Notes in Computer Science(), vol 6464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17432-2_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-17432-2_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17431-5
Online ISBN: 978-3-642-17432-2
eBook Packages: Computer ScienceComputer Science (R0)