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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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
Publisher Name: Springer, Berlin, Heidelberg
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