Collective behaviour and swarm intelligence
How do groups of small brained (or no brained) organisms solve complex optimisation problems?
Some of the most powerful and flexible optimisation algorithms to be developed in recent years have been inspired by natural systems: these include genetic algorithms, artificial neural networks and ant colony optimisation algorithms. The natural world represents a gold mine of problem solving algorithms, yet this potential is largely untapped, primarily because biologists have yet to understand the behavioural mechanisms underlying problem solving in many systems. We aim to fill this knowledge gap by dissecting the behaviours that allow ant colonies, bee hives, and slime mould amoebas to collectively make ‘smart’ decisions.
At the moment, we have one funded projects investigating collective behaviour and swarm intelligence.
1. Bio-inspired solutions for protecting critical infrastructure systems (Funded by the Branco Weiss- Society in Science Fellowship)
For more information info, see:
Latty, T., Holmes, M., Makinson, J., Beekman, M. (2017). Argentine ants (Linepithema humile) use adaptable transportation networks to track changes in resource quality. Journal of Experimental Biology, 220, 686-694.
Middleton, E., Latty, T. (2016). Resilience in social insect infrastructure systems. Journal of the Royal Society Interface, 13(116), 1-13.
Reid, C., MacDonald, H., Mann, R., Marshall, J., Latty, T., Garnier, S. (2016). Decision-making without a brain: how an amoeboid organism solves the two-armed bandit. Journal of the Royal Society Interface, 13(119), 1-8.
Reid, C. R., Latty, T., & Beekman, M. 2013. Making a trail: informed Argentine ants lead colony to the best food by U-turning coupled with enhanced pheromone laying. Animal Behaviour, in press
Reid, C. R., Beekman, M., Latty, T., & Dussutour, A. 2013. Amoeboid organism uses extracellular secretions to make smart foraging decisions. Behavioral Ecology, 24: 4, 812-818.
Reid, C. R., Latty, T., Dussutour, A. & Beekman, M. 2012. Slime mold uses an externalized spatial “memory” to navigate in complex environments. Proceedings of the National Academy of Sciences, 109, 17490-17494
Granovskiy,B, Latty,T; Duncan,M; Sumpter, D J.T, Beekman, M 2012. How dancing honey bees keep track of changes: the role of inspector bees. Behavioural Ecology,23, 588-59
Latty, T, Ramsch, K, Ito,K, Nakagaki,T, Sumpter, DJT, Middendorf, M, and Beekman,M.2011 Structure and formation of ant transportation networks. Proceedings of the royal society Interface. 8, 1298-1306