Abstract
Memristors are used to compare three gathering techniques in an already-mapped environment where resource locations are known. The All Site model, which apportions gatherers based on the modeled memristance of that path, proves to be good at increasing overall efficiency and decreasing time to fully deplete an environment, however it only works well when the resources are of similar quality. The Leafcutter method, based on Leafcutter ant behaviour, assigns all gatherers first to the best resource, and once depleted, uses the All Site model to spread them out amongst the rest. The Leafcutter model is better at increasing resource influx in the short-term and vastly out-performs the All Site model in a more varied environments. It is demonstrated that memristor based abstractions of gatherer models provide potential methods for both the comparison and implementation of agent controls.
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References
Chua, L.O.: Memristor — The Missing Circuit Element. IEEE Trans. Circuit Theory 18, 507–519 (1971)
Strukov, D.B., Snider, G.S., Stewart, D.R., Williams, R.S.: The Missing Memristor Found. Nature 453, 80–83
Bernabé, L.-B., Teresa, S.-G.: Memristance can explain Spike-Time-Dependent-Plasticity in Neural Synapses. Available from Nature Precedings (2009), http://hdl.handle.net/10101/npre.2009.3010.1
Mullins, J.: Memristor minds: The future of artificial intelligence. New Scientist, 2715
Chua, L.O., Kang, S.M.: Memristive Devices and Systems. PIEEE 64, 209–223 (1976)
Howard, D., Gale, E., Bull, L., De Lacy Costello, B., Adamatzky, A.: Evolving Spiking Networks with Variable Memristors. In: GECCO 2011: Proceedings of the Genetic and Evolutionary Computation Conference. ACM Press (2011)
Howard, D., Gale, E., Bull, L., De Lacy Costello, B., Adamatzky, A.: Towards Evolving Spiking Networks with Memristive Synapses. In: Proceedings of the IEEE Symposium on Artificial Life. IEEE (2011)
Zamarreno-Ramos, C., Camunas-Mesa, L.A., Perez-Carrasco, J.A., Masquelier, T., Serrano-Gotarredona, T., Linares-Barranco, B.: On spike-timing-dependent-plasticity memristive devices, and building a self-learning visual cortex. Frontiers in Neuroscience 5, 26.1–26.22 (2011)
Pershin, Y., Di Ventra, M.: Solving mazes with memristors: a massively-parallel approach. Available from Nature Precedings (2011), http://dx.doi.org/10.1038/npre.2011.5748.1
Tarassenko, L., Blake, A.: Analogue Computation of Collision-free paths. In: Proc. IEEE: International Conference on Robotica and Automation, pp. 540–545 (1991)
Marshall, G.F., Tarassenko, L.: Robot path planning using resistive grids
Lubbock, J.: Observations on Ants, Bees, and Wasps; with a Description of a new Species of Honey-Ant. Part VII. Ants. Journal of the Linnean Society of London, Zoology 15, 167–187 (1880)
Wilson, E.O.: Behavior of Daceton armigerum (Latreille), with a classification of self-grooming movements in ants. Bulletin of the Museum of Comparative Zoology of Harvard College 127, 403–421 (1962)
Jackson, D.E., Martin, S.J., Ratnieks, F.L.W., Holcombe, M.: Spatial and temporal variation in pheremone composition of ant foraging trails. Behavioral Ecology 18, 444–450 (2007)
Jackson, D.E., Martin, S.J., Holcombe, M., Ratnieks, F.L.W.: Longevity and detection of persistent foraging trails in Pharaoh’s ants, Monomorium pharaonis (L.). Animal Behaviour 71, 351–359 (2006)
Robinson, E.J.H., Jackson, D.E., Holcombe, M., Ratnieks, F.L.W.: ‘No entry’ signal in ant foraging. Nature 438, 442 (2005)
Sumpter, D.J.T.: The principles of collective animal behaviour. Phil. Trans. R. Soc. B 361, 5–22 (2006)
Jackson, D.E., Holcombe, M., Ratnieks, F.L.W.: Trail geometry gives polarity to ant foraging networks. Nature 432, 907–909 (2004)
Dorigo, M.M., Colorni, A.: Ant System: Optimization by a colony of cooperating agents. IEEE Trans. Sys. Man and Cybernetics B 26, 29–41 (1996)
Dorigo, M., Di Caro, G.: The Ant Colony Metaheuristic. In: New Ideas in Optimization, pp. 11–32 (1999)
Dorigo, M., Di Caro, G., Gambardella, M.: Ant algorithms for discrete optimization. Artificial Life 5, 137–172 (1999)
Stutzle, T., Hoos, H.H.: Max-min ant system. Future Generation Computer Systems 16, 889–914 (2000)
Bullnheimer, B., Hartl, R.F., Strauss, C.: A new rank-based version of hte Ant system: A computational study. Central European Journal for Operations Research and Economics 7, 25–38 (1996)
Mullen, R.J., Monekosson, D., Barman, S., Remagnino, P.: Expert Systems and Applications 36, 9608–9617 (2009)
Dorigo, M., Gambardella, L.M.: Ant colonies for the travelling salesman problem. BioSystems 43, 73–81 (1997)
Traniello, J.F.A.: Foraging Strategies of Ants. Ann. Rev. Entomol. 34, 191–210 (1989)
Taylor, F.: Foraging behavior in ants: experiments with two species of myrmecine ants. Behav. Ecol. Socio-biol. 2, 147–168 (1978)
Rockwood, L., Hubell, S.P.: Host-plant selection, diet diversity and optimal foraging in a tropical leafcutting ant. Oecologia 74, 55–61 (1987)
Gordon, D.M.: Ants at Work: how an insect society is organized. Free Press, Simon and Schuster (1999)
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Gale, E., de Lacy Costello, B., Adamatzky, A. (2012). Comparison of Ant-Inspired Gatherer Allocation Approaches Using Memristor-Based Environmental Models. In: Hart, E., Timmis, J., Mitchell, P., Nakamo, T., Dabiri, F. (eds) Bio-Inspired Models of Networks, Information, and Computing Systems. BIONETICS 2011. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32711-7_6
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DOI: https://doi.org/10.1007/978-3-642-32711-7_6
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