Abstract
The prevalence of autonomous agents has raised the need for agents to cooperate without having the ability to coordinate their moves in advance, or communicate explicitly. This is referred to as ad-hoc teamwork. Prior work in this field has examined the possibility of leading a flock of simple, swarm-like, agents to a desired behavior that maximizes joint group utility, using informed agents that act within the flock. In this work we examine the problem of leading a flock of agents using signals. In this problem, the leading agents are equipped with a tool allowing them to send a simple signal to the flock, “calling” them to act in a desired way. However, the agents may misinterpret the signal with some probability, and head to the opposite direction. We examine the best behavior for a leading agent, that is, deciding when to signal, which depends on the signaling range of the leader, the probability of the signal misinterpretation, the sensing range of the flocking agents, and their current behavior. We extend the analysis to multiple leading agents, and show that their location within the flock also plays a role in the outcome. Finally, we examine the use of signals by leading a swarm of agents in the dispersion problem, where the team’s goal is to spread in the environment and demonstrate the limitation of signals. Specifically, we show that signals may have no influence on the performance of the swarm, even if they are perfectly interpreted.
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Formal proofs and other additional material can be found in the supplamentary material in: www.cs.biu.ac.il/~agmon/DARS2021SignalSup.pdf.
References
Agmon, N., Stone, P.: Leading ad hoc agents in joint action settings with multiple teammates. In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, pp. 341–348 (2012)
Atta, S., Sinha Mahapatra, P.R., Mukhopadhyay, A.: Solving maximal covering location problem using genetic algorithm with local refinement. Soft. Comput. 22(12), 3891–3906 (2017). https://doi.org/10.1007/s00500-017-2598-3
Batalin, M.A., Sukhatme, G.S.: Spreading out: a local approach to multi-robot coverage. In: Asama, H., Arai, T., Fukuda, T., Hasegawa, T. (eds.) Distributed Autonomous Robotic Systems 5, pp. 373–382. Springer, Tokyo (2002). https://doi.org/10.1007/978-4-431-65941-9_37
Becker, M., Blatt, F., Szczerbicka, H.: A concept of layered robust communication between robots in multi-agent search & rescue scenarios. In: 2014 IEEE/ACM 18th International Symposium on Distributed Simulation and Real Time Applications, pp. 175–180 (2014)
Couzin, I.D., Krause, J., James, R., Ruxton, G.D., Franks, N.R.: Collective memory and spatial sorting in animal groups. J. Theor. Biol. 218(1), 1–11 (2002)
Das, B., Couceiro, M.S., Vargas, P.A.: MRoCS: a new multi-robot communication system based on passive action recognition. Robot. Auton. Syst. 82, 46–60 (2016)
Genter, K.L. et al.: Fly with me: algorithms and methods for influencing a flock. Ph.D. thesis (2017)
Genter, K., Agmon, N., Stone, P.: Ad hoc teamwork for leading a flock. In: Proceedings of the 2013 International Conference on Autonomous Agents and Multi-agent Systems, pp. 531–538 (2013)
Genter, K., Stone, P.: Adding influencing agents to a flock. In: Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, pp. 615–623 (2016)
Griffith, S., Subramanian, K., Scholz, J., Isbell, C.L., Thomaz, A.L.: Policy shaping: integrating human feedback with reinforcement learning. In: Advances in Neural Information Processing Systems, pp. 2625–2633 (2013)
Grizou, J., Barrett, S., Stone, P., Lopes, M.: Collaboration in ad hoc teamwork: ambiguous tasks, roles, and communication. In: AAMAS Adaptive Learning Agents (ALA) Workshop (2016)
Grizou, J., Iturrate, I., Montesano, L., Oudeyer, P.-Y., Lopes, M.: Interactive learning from unlabeled instructions. In: Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence (UAI) (2014)
Howard, A., Matarić, M.J., Sukhatme, G.S.: Mobile sensor network deployment using potential fields: a distributed, scalable solution to the area coverage problem. In: Asama, H., Arai, T., Fukuda, T., Hasegawa, T. (eds.) Distributed Autonomous Robotic Systems 5, pp. 299–308. Springer, Tokyo (2002). https://doi.org/10.1007/978-4-431-65941-9_30
Jin, K., Li, J., Wang, H., Zhang, B., Zhang, N.: Near-linear time approximation schemes for geometric maximum coverage. Theoret. Comput. Sci. 725, 64–78 (2018)
Luke, S., Cioffi-Revilla, C., Panait, L., Sullivan, K.: MASON: a new multi-agent simulation toolkit. In: Proceedings of the 2004 Swarmfest Workshop, pp. 316–327 (2004)
Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximations for maximizing submodular set functions-I. Math. Program. 14(1), 265–294 (1978)
Potop-Butucaru, M., Raynal, M., Tixeuil, S.: Distributed computing with mobile robots: an introductory survey. In: 2011 14th International Conference on Network-Based Information Systems, pp. 318–324. IEEE (2011)
Reut, M., William, M., Andy, W., Harel, Y., Peter, S.: A penny for your thoughts: the value of communication in ad hoc teamwork. In: International Joint Conference on Artificial Intelligence (IJCAI) (2020)
Stone, P., Kaminka, G.A., Rosenschein, J.S.: Leading a best-response teammate in an ad hoc team. In: David, E., Gerding, E., Sarne, D., Shehory, O. (eds.) AMEC/TADA -2009. LNBIP, vol. 59, pp. 132–146. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15117-0_10
Torrey, L., Taylor, M.: Teaching on a budget: agents advising agents in reinforcement learning. In: Proceedings of the 2013 International Conference on Autonomous Agents and Multi-agent Systems, pp. 1053–1060 (2013)
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This research was funded in part by ISF grant 2306/18.
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Menashe, N., Agmon, N. (2022). Leading a Swarm with Signals. In: Matsuno, F., Azuma, Si., Yamamoto, M. (eds) Distributed Autonomous Robotic Systems. DARS 2021. Springer Proceedings in Advanced Robotics, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-92790-5_2
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