A Reinforcement Learning Based Method for Optimizing the Process of Decision Making in Fire Brigade Agents

  • Abbas Abdolmaleki
  • Mostafa Movahedi
  • Sajjad Salehi
  • Nuno Lau
  • Luís Paulo Reis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7026)


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.


RoboCup Rescue Simulation Fire Brigade Decision Making Reinforcement Learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Abbas Abdolmaleki
    • 1
    • 2
  • Mostafa Movahedi
    • 5
  • Sajjad Salehi
    • 6
  • Nuno Lau
    • 1
    • 4
  • Luís Paulo Reis
    • 2
    • 3
  1. 1.IEETA – Institute of Electronics and Telematics Engineering of AveiroPortugal
  2. 2.LIACC – Artificial Intelligence and Computer Science Lab.PortoPortugal
  3. 3.DEI/FEUP – Informatics Engineering Dep., Faculty of EngineeringUniv. of PortoPortugal
  4. 4.DETI/UA – Electronics, Telecommunications and Informatics Dep.Univ. of AveiroPortugal
  5. 5.Department of Computer EngineeringSheikh Bahaee UniversityIsfahanIran
  6. 6.Young researchers club, Qazvin BranchIslamic Azad UniversityQazvinIran

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