Human Swarming with Artificial Swarm Intelligence using a hybrid approach
Keywords:
Swarm intelligence., Human Swarming, ASI AlgorithmsAbstract
Swarm Intelligence explores swarms of autonomous robots or simulated agents. Little work, however, has been done on swarms of networked humans. Artificial Swarm Intelligence (ASI) strives to facilitate the emergence of a super-human intellect by connecting groups of human users in closed-loop systems modeled after biological swarms. Early studies have shown that “human swarms” can make more accurate predictions than traditional methods for tapping the wisdom of groups, such as votes and polls. Artificial Swarm Intelligence enables groups to form real-time systems online, connecting as ‘human swarms’ from anywhere in the world. A combination of real-time human input and A.I. algorithms, a Swarm Artificial Swarm Intelligence based system combines the knowledge, wisdom, opinions, and intuitions of live human participants as a unified emergent intelligence that can generate optimized predictions, decisions, insights, and judgments. Simply put, Swarm A.I. technology creates amplified intelligence while keeping humans in the loop
References
[1] Beni, G., Wang, J. Swarm Intelligence in Cellular Robotic Systems, Proceed. NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy (1989).
[2] Rosenberg, L.B, “Human Swarms, a real-time paradigm for collective intelligence.” Collective Intelligence 2015, Santa Clara CA.
[3] Rosenberg, L.B., “Human Swarms, a real-time method for collective intelligence.” Proceedings of the European Conference on Artificial Life 2015, pp. 658-659
[4] Seeley, Thomas D., Visscher, P. Kirk. Choosing a home: How the scouts in a honey bee swarm perceive the completion of their group decision making. Behavioral Ecology and Sociobiology 54 (5) 511-520.
[5] Seeley, Thomas D. Honeybee Democracy. Princeton University Press, 2010.
[6] Seeley, Thomas D., et al. "Stop signals provide cross inhibition in collective decision-making by honeybee swarms." Science 335.6064 (2012): 108-111.
[7] Axelrod R, Hamilton WD (1981) The evolution of cooperation. Science 211:1390–1396.
[8] Greene, Joshua (2013). Moral Tribes: Emotion, Reason, and the Gap between Us and Them. Penguin Press.
[9] Rosenberg, L.B, et al. “Swarm Intelligence and Morality of the Hive Mind” Collective Intelligence 2016, Santa Clara CA.
[10] Zhu, f.Yen, et al. “overview of Swarm intelligence” ICCASM, 22-24 Oct. 2010.
[11] Karasi, A., et al. “ Finding safe path and locations in disaster affected area using Swarm Intelligence” International Conference on Emerging Trends in Communication Technologies (ETCT), 2016.
[12] Lev Muchnik, Sinan Aral, Sean J. Taylor. Social Influence Bias: A Randomized Experiment. Science, 9 August 2013: Vol. 341 no. 6146 pp. 647-651.
[13] Rand, D. G., Arbesman, S. & Christakis, N. A. (2011) Dynamic social networks promote cooperation in experiments with humans. Proc. Natl Acad. Sci. USA 108, 19193–19198.
[14] Pinheiro, F. L., Santos, F. C., and Pacheco, J. M. (2012). How selection pressure changes the nature of social dilemmas in structured populations. New J. Phys., 14(7):073035.
[15] Santos, F. C., Pinheiro, F. L., Lenaerts, T., and Pacheco, J. M. (2012). The role of diversity in the evolution of cooperation. J. Theor. Biol., 299:88–96.
[16] Eberhart, Russell, Daniel Palmer, and Marc Kirschenbaum "Beyond computational intelligence: blended intelligence." Swarm/Human Blended Intelligence Workshop (SHBI), 2015. IEEE, 2015.
[17] K.m. Passino, T.F. Seeley, P.K. Visscher, Swarm Cognition in honeybees, Behav. Ecol. Sociobiol. 62, 401 (2008).
[18] J.A.R. Marchall, R. Bogacz, A. Dornhaus, R. Planque, T.Kovacs, N.R. Franks, On optimal decision making in brains and social insect colonies, J.R. Soc Interface 6,1065 (2009).
[19] I.D. Couzin, Collective Cognition in Animal Groups, Trends Cogn. Sci. 13,36 (2008).
[20] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on NeuralNetworks, pp. 1942–1948, December 1995.
[21] Y. Shi and R. Eberhart, “Modified particle swarm optimizer,” in Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC ’98), pp. 69–73, May 1998.
[22] Y. Shi and R. C. Eberhart, “Fuzzy adaptive particle swarm optimization,” in Proceedings of the Congress on Evolutionary Computation, pp. 101–106, May 2001.
[23] Varinder Singh et.al. ,"A Effective Decision Making Approach “Human Swarming with Artificial Swarm Intelligence”" International Journal of Advanced Research in Computer Science,Volume 8, No. 4, May 2017 (Special Issue).
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