Aggregation as a Simple Seed for Collective Decision
Pages 39 - 43
Abstract
For Artificial Intelligence, nature is offering abundant sources of inspiration in the form of collective behaviours, performed by myriads of living creatures, ranging from the microscopic bacteria, to the macroscopic herds of mammals, schools of fish and bird flocks, not to mention social insects, from which the newly established field of Swarm Intelligence borrows it's name.
Decentralized animal collectives rely in their decision-making mechanisms on self-organizing principles which they have their applications into the control and optimization mechanisms in Computer Science; They foster the design of newly robust and adaptive optimization and coordination algorithms of multi-agent systems.
The goal of this work is to investigate a self-organising behaviour attributed to cockroaches, called aggregation and ascertain through computer simulations whether aggregation can lead the group of cockroaches to collectively choose a shelter among more than two shelters. Garnier et al. [7] had carried a similar work with two aggregation shelters only. In this paper, we investigate the aggregation behaviour based on the hybrid model and ascertain through simulations whether it can lead the group of cockroaches to collectively choose a shelter among more than two shelters.
Simulations are based on a hybrid model of two previously and separately devised self-organizing models, which are: Jeanson et al.[2] behavioural model that describes the individual behaviours of cockroaches in a stochastic framework and Couzin et al.[4] model on animal's movements in a three-dimensional space.
References
[1]
Smith W.V. Treuil J.P. Adioui M., Arino O. 2003. A mathematical analysis of a fish school model. Differential Equations 188, 2 (2003), 406--446.
[2]
Deneubourg J. L. Fourcassi V. Theraulaz G. Blanco S., Fournier R. 2003. A model of animal movements in a bounded space. Theoretical Biology (2003).
[3]
Anderson C. 2002. Self-organization System in Relation to Several Similar Concepts: Are the Boundaries to Self-Organization Indistinct? D-93040 Regensbourg, Germany 202, 3 (2002), 247--255.
[4]
James R. Ruxton G.D. Franks N.R. Couzin I.D., Krause J. 2002. Collective Memory and Spatial Sorting in Animal Groups. Theoretical Biology 218 (2002), 1--11.
[5]
Holvoet T. De Wolf T. 2004. Emergence and Self-Organisation: A statement of similarities and differences. In Proceedings of the Second International Workshop on Engineering SelfOrganising Applications. 96--110.
[6]
Deneubourg J.-L. Depickére S., Fresneau D. 2004. Dynamics of aggregation in Lasius niger (Formicidae): influence of polyethism, Insectes Soc. 51 (2004), 110.
[7]
Gautrais J.-Asadpour M. Caprari G. Jeanson R. Grimal A. Theraulaz G. Garnier S., Jost C. 2008. The embodiment of cockroach aggregation behavior in a group of micro-robots. Artificial Life 14, 4 (2008), 387--408.
[8]
Tormos J. Giovanetti M., JD Asís. Living in Aggregations: Theories and Facts in the Life of Hymenoptera. American Mathematical Society.
[9]
Francis H. 2008. Complexity and Self-organization. Free University of Brussels, Belgium.
[10]
Jan M.M. Hai-Tao Z., Michael Z.C. 2008. Collective Behavior Coordination with Predictive Mechanisms. IEEE Circuits and Systems Magazine.
[11]
Robert F.J. Kaushik J. 2015. Cockroaches Traverse Crevices, Crawl Rapidly in Confined Spaces, and Inspire a Soft, Legged Robot. In Proceedings of the National Academy of Sciences of the United States of America.
[12]
Joel P.H. Leah E.K. Nessy T., Ben V. 2012. Role of Social Interactions in Dynamic Patterns of Resource Patches and Forager Aggregation. In Proceedings of the National Academy of Sciences, Vol. 109. USA, 11228--11233.
[13]
Timothy T. Ross A.C., Mark B. 2004. Aggregation as a Defense: Limpet Tenacity Changes in Response to Simulated Predator Attack. Ecological Society of America 85, 4 (2004), 1153--1159.
[14]
Sheli S.C. Sangita R., Samir B. 2014. Nature-Inspired Swarm Intelligence and Its Applications. International Journal of Modern Education and Computer Science 6, 12 (2014).
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Published In
November 2016
163 pages
ISBN:9781450348768
DOI:10.1145/3038884
- General Chairs:
- Chawki Djeddi,
- Imran Siddiqi,
- Akram Bennour,
- Program Chairs:
- Youcef Chibani,
- Haikal El Abed
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Published: 22 November 2016
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MedPRAI-2016
MedPRAI-2016: Mediterranean Conference on Pattern Recognition and Artificial Intelligence
November 22 - 23, 2016
Tebessa, Algeria
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