A reinforcement learning based method for optimizing the process of decision making in fire brigade agents

Authors:
Abbas Abdolmaleki;Mostafa Movahedi;Sajjad Salehi;Nuno Lau;Luís Paulo Reis
Affiliations:
Institute of Electronics and Telematics Engineering of Aveiro and Artificial Intelligence and Computer Science Lab., Porto, Portugal;Sheikh Bahaee University, Department of Computer Engineering, Isfahan, Iran;Young researchers club, Qazvin branch, Islamic azad university, Qazvin, Iran;Institute of Electronics and Telematics Engineering of Aveiro and Electronics, Telecommunications and Informatics Dep., Univ. of Aveiro, Portugal;Artificial Intelligence and Computer Science Lab. and Informatics Engineering Dep., Faculty of Engineering, Univ. of Porto, Portugal
Venue:
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Year:
2011

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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.