Abstract
Decision making in complex, multi-agent and dynamic environments such as disaster spaces is a challenging problem in Artificial Intelligence. This research paper aims at developing distributed coordination and cooperation method based on reinforcement learning to enable team of homogeneous, autonomous fire fighter agents, with similar skills to accomplish complex task allocation, with emphasis on firefighting tasks in disaster space. The main contribution is applying reinforcement learning to solve the bottleneck caused by dynamicity and variety of conditions in such situations as well as improving the distributed coordination of fire fighter agent’s to extinguish fires within a disaster zone. The proposed method increases the speed of learning; it has very low memory usage and has a good scalability and robustness in the case that the number of agents and complexity of task increases. The effectiveness of the proposed method is shown through simulation results.
Chapter PDF
Similar content being viewed by others
Keywords
References
Reis, L.P., Lau, N., Oliveira, E.C.: Situation Based Strategic Positioning for Coordinating a Team of Homogeneous Agents. In: Hannebauer, M., Wendler, J., Pagello, E. (eds.) Reactivity and Deliberation in MAS. LNCS (LNAI), vol. 2103, pp. 175–197. Springer, Heidelberg (2001)
Stone, P., Veloso, M.: Task Decomposition, Dynamic Role Assignment, and Low-Bandwidth Communication for Real-Time Strategic Teamwork. Artificial Intelligence 110(2), 241–273 (1999)
Stone, P., Veloso, M.: Layered approach to learning client behaviors in the robocup soccer server. Applied Artificial Intelligence 12(2-3) (1998)
Kleiner, A., Dietl, M., Nebel, B.: Towards a Life-Long Learning Soccer Agent. In: Kaminka, G.A., Lima, P.U., Rojas, R. (eds.) RoboCup 2002. LNCS (LNAI), vol. 2752, pp. 126–134. Springer, Heidelberg (2003)
Jennings, N.R.: Controlling Cooperative Problem Solving in Industrial Multiagent Systems using Joint Intentions. Artificial Intelligence 75(2), 195–240 (1995)
Lekavý, M., Návrat, P.: Expressivity of STRIPS-Like and HTN-Like Planning. In: Nguyen, N.T., Grzech, A., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2007. LNCS (LNAI), vol. 4496, pp. 121–130. Springer, Heidelberg (2007)
Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.): RoboCup 2005. LNCS (LNAI), vol. 4020. Springer, Heidelberg (2006)
Lakemeyer, G., Sklar, E., Sorrenti, D.G., Takahashi, T. (eds.): RoboCup 2006. LNCS (LNAI), vol. 4434. Springer, Heidelberg (2007)
Visser, U., Ribeiro, F., Ohashi, T., Dellaert, F. (eds.): RoboCup 2007. LNCS (LNAI), vol. 5001. Springer, Heidelberg (2008)
Kok, J.R., Spaan, M.T.J., Vlassis, N.: Multi-Robot Decision Making using Coordination Graphs. In: Proceedings of 11th International Conference on Advanced Robotics (ICAR), Coimbra, Portugal, pp. 1124–1129 (2003)
Paquet, S., Bernier, N., Chaib-draa, B.: Comparison of different coordination strategies for the roboCupRescue simulation. In: Orchard, B., Yang, C., Ali, M. (eds.) IEA/AIE 2004. LNCS (LNAI), vol. 3029, pp. 987–996. Springer, Heidelberg (2004)
Mohammadi, Y.B., Tazari, A., Mehrandezh, M.: A new hybrid task sharing method for cooperative multi agent systems. In: Canadian Conf. on Electrical and Computer Engineering (May 2005)
Martínez, I.C., Ojeda, D., Zamora, E.A.: Ambulance decision support using evolutionary reinforcement learning in robocup rescue simulation league. In: Lakemeyer, G., Sklar, E., Sorrenti, D.G., Takahashi, T. (eds.) RoboCup 2006. LNCS (LNAI), vol. 4434, pp. 556–563. Springer, Heidelberg (2007)
Paquet, S., Bernier, N., Chaib-draa, B.: From global selective perception to local selective perception. In: AAMAS, pp. 1352–1353 (2004)
Amraii, S.A., Behsaz, B., Izadi, M.: S.o.s 2004: An attempt towards a multi-agent rescue team. In: Proc. 8th RoboCup Int’l Symposium (2004)
Abdolmaleki, A., Movahedi, M., Salehi, S., Lau, N., Reis, L.P.: A Reinforcement Learning Based Method for Optimizing the Process of Decision Making in Fire Brigade Agents. In: Antunes, L., Pinto, H.S. (eds.) EPIA 2011. LNCS, vol. 7026, pp. 340–351. Springer, Heidelberg (2011)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Abdolmaleki, A., Movahedi, M., Lau, N., Reis, L.P. (2013). A Distributed Cooperative Reinforcement Learning Method for Decision Making in Fire Brigade Teams. In: Chen, X., Stone, P., Sucar, L.E., van der Zant, T. (eds) RoboCup 2012: Robot Soccer World Cup XVI. RoboCup 2012. Lecture Notes in Computer Science(), vol 7500. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39250-4_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-39250-4_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39249-8
Online ISBN: 978-3-642-39250-4
eBook Packages: Computer ScienceComputer Science (R0)