Abstract
We introduce the concept of cognitive design pattern to provide a design methodology for distributed multi-agent systems. A cognitive design pattern is a reusable solution to tackle problems requiring cognitive abilities (e.g., decision-making, attention, categorisation). It provides theoretical models and design guidelines to define the individual control rules in order to obtain a desired behaviour for the multi-agent system as a whole. In this paper, we propose a cognitive design pattern for collective decision-making inspired by the nest-site selection behaviour of honeybee swarms. We illustrate how to apply the pattern to a case study involving spatial factors: the collective selection of the shortest path between two target areas. We analyse the dynamics of the multi-agent system and we show a very good agreement with the predictions of the macroscopic model.
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References
Babaoğlu, O., Canright, G., Deutsch, A., Di Caro, G., Ducatelle, F., Gambardella, L.M., Ganguly, N., Jelasity, M., Montemanni, R., Montresor, A., Urnes, T.: Design patterns from biology for distributed computing. Transactions on Adaptive and Autonomous Systems 1(1), 26–66 (2006)
Campo, A., Garnier, S., Dédriche, O., Zekkri, M., Dorigo, M.: Self-Organized Discrimination of Resources. PLoS One 6(5), e19888 (2011)
Codling, E.A., Plank, M.J., Benhamou, S.: Random walk models in biology. Journal of the Royal Society, Interface 5(25), 813–834 (2008)
Couzin, I.: Collective cognition in animal groups. Trends in Cognitive Sciences 13(1), 36–43 (2009)
Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley Professional (1995)
Gardelli, L., Viroli, M., Omicini, A.: Design patterns for self-organising systems. In: Burkhard, H.-D., Lindemann, G., Verbrugge, R., Varga, L.Z. (eds.) CEEMAS 2007. LNCS (LNAI), vol. 4696, pp. 123–132. Springer, Heidelberg (2007)
Hamann, H.: Towards swarm calculus: Urn models of collective decisions and universal properties of swarm performance. Swarm Intelligence 7(2-3), 145–172 (2013)
Marshall, J.A.R., Bogacz, R., Dornhaus, A., Planqué, R., Kovacs, T., Franks, N.R.: On optimal decision-making in brains and social insect colonies. Journal of the Royal Society, Interface 6(40), 1065–1074 (2009)
Montes, M., Ferrante, E., Scheidler, A., Pinciroli, C., Birattari, M., Dorigo, M.: Majority-rule opinion dynamics with differential latency: A mechanism for self-organized collective decision-making. Swarm Intelligence 5(3-4), 305–327 (2010)
Nelson, W.: Hazard plotting for incomplete failure data. Journal of Quality Technology 1, 27–52 (1969)
Pais, D., Hogan, P.M., Schlegel, T., Franks, N.R., Leonard, N.E., Marshall, J.A.R.: A mechanism for value-sensitive decision-making. PLoS One 8(9), e73216 (2013)
Parker, C.A.C., Zhang, H.: Cooperative decision-making in decentralized multiple-robot systems: the best-of-N problem. IEEE Transactions on Mechatronics 14(2), 240–251 (2009)
Seeley, T.D., Visscher, P.K., Schlegel, T., Hogan, P.M., Franks, N.R., Marshall, J.A.R.: Stop signals provide cross inhibition in collective decision-making by honeybee swarms. Science 335(6064), 108–111 (2012)
Trianni, V., Tuci, E., Passino, K.M., Marshall, J.A.R.: Swarm Cognition: An interdisciplinary approach to the study of self-organising biological collectives. Swarm Intelligence 5(1), 3–18 (2010)
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Reina, A., Dorigo, M., Trianni, V. (2014). Towards a Cognitive Design Pattern for Collective Decision-Making. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2014. Lecture Notes in Computer Science, vol 8667. Springer, Cham. https://doi.org/10.1007/978-3-319-09952-1_17
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DOI: https://doi.org/10.1007/978-3-319-09952-1_17
Publisher Name: Springer, Cham
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