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HuGoS: A Multi-user Virtual Environment for Studying Human–Human Swarm Intelligence

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Swarm Intelligence (ANTS 2020)

Abstract

The research topic of human–human swam intelligence includes many mechanisms that need to be studied in controlled experiment conditions with multiple human subjects. Virtual environments are a useful tool to isolate specific human interactions for study, but current platforms support only a small scope of possible research areas. In this paper, we present HuGoS—‘Humans Go Swarming’—a multi-user virtual environment in Unity, as a comprehensive tool for experimentation in human–human swarm intelligence. We identify possible experiment classes for studying human collective behavior, and equip our virtual environment with sufficient features to support each of these experiment classes. We then demonstrate the functionality of the virtual environment in simple examples for three of the experiment classes: human collective decision making, human social learning strategies, and agent-level human interaction with artificial swarms, including robot swarms.

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References

  1. Bailenson, J.N., Beall, A.C., Loomis, J., Blascovich, J., Turk, M.: Transformed social interaction: decoupling representation from behavior and form in collaborative virtual environments. Presence Teleoperators Virtual Environ. 13(4), 428–441 (2004). https://doi.org/10.1162/1054746041944803

    Article  Google Scholar 

  2. Barrington, L., et al.: Crowdsourcing earthquake damage assessment using remote sensing imagery. Ann. Geophys. 54(6) (2011). https://doi.org/10.4401/ag-5324

  3. Blascovich, J., Loomis, J., Beall, A., Swinth, K., Hoyt, C., Bailenson, J.: Immersive virtual environment technology as a methodological tool for social psychology. Psychol. Inq. 13, 103–124 (2002)

    Article  Google Scholar 

  4. Boos, M., Pritz, J., Lange, S., Belz, M.: Leadership in moving human groups. PLoS Comput. Biol. 10(4), e1003541 (2014). https://doi.org/10.1371/journal.pcbi.1003541

    Article  Google Scholar 

  5. Cheng, J.T., Tracy, J.L., Foulsham, T., Kingstone, A., Henrich, J.: Two ways to the top: evidence that dominance and prestige are distinct yet viable avenues to social rank and influence. J. Pers. Soc. Psychol. 104(1), 103–125 (2013). https://doi.org/10.1037/a0030398

    Article  Google Scholar 

  6. Cooper, S., et al.: Predicting protein structures with a multiplayer online game. Nature 466(7307), 756–760 (2010). https://doi.org/10.1038/nature09304

    Article  Google Scholar 

  7. De Montjoye, Y.A., Stopczynski, A., Shmueli, E., Pentland, A., Lehmann, S.: The strength of the strongest ties in collaborative problem solving. Sci. Rep. 4, 5277 (2014)

    Article  Google Scholar 

  8. Ducatelle, F., et al.: Cooperative navigation in robotic swarms. Swarm Intell. 8(1), 1–33 (2013). https://doi.org/10.1007/s11721-013-0089-4

    Article  Google Scholar 

  9. Eberhart, R., Palmer, D., Kirschenbaum, M.: Beyond computational intelligence: blended intelligence. In: 2015 Swarm/Human Blended Intelligence Workshop (SHBI). IEEE (2015). https://doi.org/10.1109/shbi.2015.7321679

  10. Henrich, J., Heine, S.J., Norenzayan, A.: The weirdest people in the world? Behav. Brain Sci. 33(2–3), 61–83 (2010). https://doi.org/10.1017/s0140525x0999152x

    Article  Google Scholar 

  11. Heyes, C.: Who knows? Metacognitive social learning strategies. Trends Cogn. Sci. 20(3), 204–213 (2016). https://doi.org/10.1016/j.tics.2015.12.007

    Article  Google Scholar 

  12. Hunt, E.R., Jones, S., Hauert, S.: Testing the limits of pheromone stigmergy in high-density robot swarms. Roy. Soc. Open Sci. 6(11), 190225 (2019). https://doi.org/10.1098/rsos.190225

    Article  Google Scholar 

  13. Ioannou, C.C.: Swarm intelligence in fish? The difficulty in demonstrating distributed and self-organised collective intelligence in (some) animal groups. Behav. Process. 141(2), 141–151 (2017)

    Article  Google Scholar 

  14. Juliani, A., et al.: Unity: a general platform for intelligent agents. arXiv preprint arXiv:1809.02627 (2018). https://arxiv.org/pdf/1809.02627.pdf

  15. Jung, J.H., Schneider, C., Valacich, J.: Enhancing the motivational affordance of information systems: the effects of real-time performance feedback and goal setting in group collaboration environments. Manage. Sci. 56(4), 724–742 (2010). https://doi.org/10.1287/mnsc.1090.1129

    Article  Google Scholar 

  16. Kalma, A.P., Visser, L., Peeters, A.: Sociable and aggressive dominance: personality differences in leadership style? Leadersh. Quart. 4(1), 45–64 (1993). https://doi.org/10.1016/1048-9843(93)90003-c

    Article  Google Scholar 

  17. Kendal, R.L., Boogert, N.J., Rendell, L., Laland, K.N., Webster, M., Jones, P.L.: Social learning strategies: bridge-building between fields. Trends Cogn. Sci. 22(7), 651–665 (2018). https://doi.org/10.1016/j.tics.2018.04.003

    Article  Google Scholar 

  18. Kirschenbaum, M., Palmer, D.W.: Perceptualization of particle swarm optimization. In: 2015 Swarm/Human Blended Intelligence Workshop (SHBI). IEEE (2015). https://doi.org/10.1109/shbi.2015.7321681

  19. Krafft, P.M., et al.: Human collective intelligence as distributed Bayesian inference. arXiv preprint arXiv:1608.01987 (2016). https://arxiv.org/pdf/1608.01987.pdf

  20. Krause, J., Ruxton, G.D., Krause, S.: Swarm intelligence in animals and humans. Trends in Ecol. Evol. 25(1), 28–34 (2010). https://doi.org/10.1016/j.tree.2009.06.016

    Article  Google Scholar 

  21. Kurvers, R.H.J.M., Wolf, M., Naguib, M., Krause, J.: Self-organized flexible leadership promotes collective intelligence in human groups. Roy. Soc. Open Sci. 2(12), 150222 (2015). https://doi.org/10.1098/rsos.150222

    Article  Google Scholar 

  22. Lepri, B., Staiano, J., Shmueli, E., Pianesi, F., Pentland, A.: The role of personality in shaping social networks and mediating behavioral change. User Model. User-Adap. Interact. 26(2–3), 143–175 (2016). https://doi.org/10.1007/s11257-016-9173-y

    Article  Google Scholar 

  23. Lin, A.Y.M., Huynh, A., Lanckriet, G., Barrington, L.: Crowdsourcing the unknown: the satellite search for Genghis Khan. PLoS ONE 9(12), e114046 (2014). https://doi.org/10.1371/journal.pone.0114046

    Article  Google Scholar 

  24. Mathews, N., Christensen, A.L., O’Grady, R., Mondada, F., Dorigo, M.: Mergeable nervous systems for robots. Nat. Commun. 8(439) (2017). https://doi.org/10.1038/s41467-017-00109-2

  25. Mekler, E.D., Brühlmann, F., Tuch, A.N., Opwis, K.: Towards understanding the effects of individual gamification elements on intrinsic motivation and performance. Comput. Hum. Behav. 71, 525–534 (2017). https://doi.org/10.1016/j.chb.2015.08.048

    Article  Google Scholar 

  26. Metcalf, L., Askay, D.A., Rosenberg, L.B.: Keeping humans in the loop: pooling knowledge through artificial swarm intelligence to improve business decision making. Calif. Manage. Rev. 61(4), 84–109 (2019)

    Article  Google Scholar 

  27. Michel, O.: Cyberbotics Ltd., Webots\(^\text{TM}\): professional mobile robot simulation. Int. J. Adv. Robot. Syst. 1(1), 40–43 (2004)

    Google Scholar 

  28. Millard, A.G., et al.: The Pi-puck extension board: a Raspberry Pi interface for the e-puck robot platform. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 741–748. IEEE (2017)

    Google Scholar 

  29. Mondada, F., et al.: The e-puck, a robot designed for education in engineering. In: Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions, vol. 1, pp. 59–65. IPCB: Instituto Politécnico de Castelo Branco (2009)

    Google Scholar 

  30. Montes de Oca, M.A., 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 Intell. 5(3–4), 305–327 (2011). https://doi.org/10.1007/s11721-011-0062-z

  31. Moussaïd, M., Helbing, D., Garnier, S., Johansson, A., Combe, M., Theraulaz, G.: Experimental study of the behavioural mechanisms underlying self-organization in human crowds. Proc. Roy. Soc. B Biol. Sci. 276(1668), 2755–2762 (2009)

    Article  Google Scholar 

  32. Moussaïd, M., et al.: Crowd behaviour during high-stress evacuations in an immersive virtual environment. J. Roy. Soc. Interface 13(122), 20160414 (2016). https://doi.org/10.1098/rsif.2016.0414

    Article  Google Scholar 

  33. Mulders, D., De Bodt, C., Bjelland, J., Pentland, A., Verleysen, M., de Montjoye, Y.A.: Inference of node attributes from social network assortativity. Neural Comput. Appl. 1–21 (2019). https://doi.org/10.1007/s00521-018-03967-z

  34. Nakamura, J., Csikszentmihalyi, M.: The concept of flow. Flow and the Foundations of Positive Psychology, pp. 239–263. Springer, Dordrecht (2014). https://doi.org/10.1007/978-94-017-9088-8_16

    Chapter  Google Scholar 

  35. Pedersen, M.K., Rasmussen, N.R., Sherson, J.F., Basaiawmoit, R.V.: Leaderboard effects on player performance in a citizen science game. In: Proceedings of the 11th European Conference on Game Based Learning, vol. 531 (2017)

    Google Scholar 

  36. Pinciroli, C., Talamali, M.S., Reina, A., Marshall, J.A.R., Trianni, V.: Simulating Kilobots within ARGoS: models and experimental validation. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A.L., Reina, A., Trianni, V. (eds.) ANTS 2018. LNCS, vol. 11172, pp. 176–187. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00533-7_14

    Chapter  Google Scholar 

  37. Pinciroli, C., et al.: ARGoS: a modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intell. 6(4), 271–295 (2012). https://doi.org/10.1007/s11721-012-0072-5

    Article  Google Scholar 

  38. Quigley, M., et al.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3, p. 5, Kobe, Japan (2009)

    Google Scholar 

  39. Quinn, A.J., Bederson, B.B.: Human computation: a survey and taxonomy of a growing field. In: Proceedings of the International Conference on Human Factors in Computing Systems (2011)

    Google Scholar 

  40. Rosenberg, L., Baltaxe, D., Pescetelli, N.: Crowds vs swarms, a comparison of intelligence. In: 2016 Swarm/Human Blended Intelligence Workshop (SHBI). IEEE (2016). https://doi.org/10.1109/shbi.2016.7780278

  41. Rosenberg, L.B.: Human swarms, a real-time method for collective intelligence. In: 20/07/2015–24/07/2015. The MIT Press (2015). https://doi.org/10.7551/978-0-262-33027-5-ch117

  42. Rubenstein, M., Cornejo, A., Nagpal, R.: Programmable self-assembly in a thousand-robot swarm. Science 345(6198), 795–799 (2014). https://doi.org/10.1126/science.1254295

    Article  Google Scholar 

  43. Sørensen, J.J.W.H., et al.: Exploring the quantum speed limit with computer games. Nature 532(7598), 210–213 (2016). https://doi.org/10.1038/nature17620

    Article  Google Scholar 

  44. Sørensen, J.J.W., et al.: Exploring the quantum speed limit with computer games. Nature 532(7598), 210–213 (2016)

    Article  Google Scholar 

  45. Thrash, T., et al.: Evaluation of control interfaces for desktop virtual environments. Presence Teleoperators Virtual Environ. 24(4), 322–334 (2015). https://doi.org/10.1162/pres_a_00237

    Article  Google Scholar 

  46. Valentini, G., Ferrante, E., Dorigo, M.: The best-of-n problem in robot swarms: formalization, state of the art, and novel perspectives. Front. Robot. AI 4 (2017). https://doi.org/10.3389/frobt.2017.00009

  47. Valentini, G., Hamann, H., Dorigo, M.: Self-organized collective decision-making in a 100-robot swarm. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015), pp. 4216–4217. AAAI Press (2015)

    Google Scholar 

  48. Vasile, C., Pavel, A., Buiu, C.: Integrating human swarm interaction in a distributed robotic control system. In: 2011 IEEE International Conference on Automation Science and Engineering, pp. 743–748. IEEE (2011)

    Google Scholar 

  49. Walker, P., Amraii, S.A., Chakraborty, N., Lewis, M., Sycara, K.: Human control of robot swarms with dynamic leaders. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1108–1113. IEEE (2014)

    Google Scholar 

  50. Wang, H., Sun, C.T.: Game reward systems: gaming experiences and social meanings. In: Proceedings of DiGRA 2011 Conference: Think Design Play (2012)

    Google Scholar 

  51. Werfel, J., Petersen, K., Nagpal, R.: Designing collective behavior in a termite-inspired robot construction team. Science 343(6172), 754–758 (2014). https://doi.org/10.1126/science.1245842

    Article  Google Scholar 

  52. Whiten, A., Hinde, R.A., Laland, K.N., Stringer, C.B.: Culture evolves. Philos. Trans. Roy. Soc. B Biol. Sci. 366(1567), 938–948 (2011). https://doi.org/10.1098/rstb.2010.0372

    Article  Google Scholar 

  53. Zhao, H., et al.: A networked desktop virtual reality setup for decision science and navigation experiments with multiple participants. J. Vis. Exp. 138(e58155) (2018). https://doi.org/10.3791/58155

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Acknowledgements

This work was partially supported by the program of Concerted Research Actions (ARC) of the Université libre de Bruxelles. M.K. Heinrich, A. Cleeremans and M. Dorigo acknowledge support from the F.R.S.-FNRS, of which they are, respectively, postdoctoral researcher and research directors.

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Correspondence to Nicolas Coucke .

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Coucke, N., Heinrich, M.K., Cleeremans, A., Dorigo, M. (2020). HuGoS: A Multi-user Virtual Environment for Studying Human–Human Swarm Intelligence. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2020. Lecture Notes in Computer Science(), vol 12421. Springer, Cham. https://doi.org/10.1007/978-3-030-60376-2_13

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  • DOI: https://doi.org/10.1007/978-3-030-60376-2_13

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