Abstract
Decision making in complex, multi agent and dynamic environments such as disaster spaces is a challenging problem in Artificial Intelligence. Uncertainty, noisy input data and stochastic behavior which are common characteristics of such environment makes real time decision making more complicated. In this paper an approach to solve the bottleneck of dynamicity and variety of conditions in such situations based on reinforcement learning is presented. This method is applied to RoboCup Rescue Simulation Fire brigade agent’s decision making process and it learned a good strategy to save civilians and city from fire. The utilized method increases the speed of learning and it has very low memory usage. The effectiveness of the proposed method is shown through simulation results.
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References
Kitano, H., Tadokoro, S.: RoboCup rescue: A grand challenge for multiagent and intelligent systems. AI Magazine 22(1), 39–52 (2001)
Takeshi, M.: How to develop a RoboCupRescue agent (2000)
Nanjanath, M., Erlandson, A.J., Andrist, S., Ragipindi, A., Mohammed, A.A., Sharma, A.S., Gini, M.: Decision and Coordination Strategies for RoboCup Rescue Agents. In: Ando, N., Balakirsky, S., Hemker, T., Reggiani, M., von Stryk, O. (eds.) SIMPAR 2010. LNCS, vol. 6472, pp. 473–484. Springer, Heidelberg (2010)
Fave, F.M.D., Packer, H., Pryymak, O., Stein, S., Stranders, u., Tran-Thanh, L., Vytelingum, P., Williamson, S.A., Jennings, N.R.: RoboCupRescue 2010 Rescue Simulation League Team Description IAMRescue, United Kingdom) (2010)
Shahbazi, H., Zafarani, R.: Priority Extraction Using Delayed Rewards in Multi Agents Systems: A Case Study in RoboCup. In: CSICC 2006, Iran, pp. 571–574 (2006)
Shahgholi Ghahfarokhi, B., Shahbazi, H., Kazemifard, M., Zamanifar, K.: Evolving Fuzzy Neural Network Based Fire Planning in Rescue Firebrigade Agents. In: SCSC 2006, Canada (2006)
Paquet, S., Bernier, N., Chaib-draa, B.: Comparison of Different Coordination Strategies for the RoboCup Rescue 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: Robot Soccer World Cup X. 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)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
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Abdolmaleki, A., Movahedi, M., Salehi, S., Lau, N., Reis, L.P. (2011). A Reinforcement Learning Based Method for Optimizing the Process of Decision Making in Fire Brigade Agents. In: Antunes, L., Pinto, H.S. (eds) Progress in Artificial Intelligence. EPIA 2011. Lecture Notes in Computer Science(), vol 7026. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24769-9_25
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DOI: https://doi.org/10.1007/978-3-642-24769-9_25
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