Delayed Rewards vs. Evolving Fuzzy Neural Network, for Firebrigades in RoboCupRescue Simulation

Abstract:

RoboCupRescue Simulation System is a platform for designing and implementing various artificial intelligent issues, and it is a very good test bed for solving Multiagent problems. One of the most important aspects of agent design in AI is the way agent acts or responds to the environment that the agent acting upon. An effective action selection and behavioral method requires a good priority extraction method for finding the best actions. In rescue simulation environments, Firebrigades should select fire points in a collaborative manner such that the total achieved result is optimized. In this work we are going to compare two different methods of fire selection in Firebrigade agents in RoboCupRescue Simulation. The first one is priority extraction using delayed rewards and the other one is Fuzzy Neural Network Based Fire Planning.

• Periodical: Advanced Materials Research (Volumes 433-440)
• Pages: 917-921
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.433-440.917
• Online since: January 2012
• Authors: Hamed Shahbazi, Sajjad Salehi, Mostafa Movahedi
• Keywords: Delayed Rewards, Evolving Fuzzy Neural Networks, Fire Selection, Firebrigade Agent, Multi-Agent System (MAS), Priority Extraction, RoboCupRescue

References

[1] Shahbazi, H., Zafarani, R. 2006. Priority Extraction Using Delayed Rewards in Multi Agents Systems: A Case Study in RoboCup". CSICC, 06, Iran, 571- 574.

[2] Shahgholi Ghahfarokhi, B. Shahbazi, H. Kazemifard, M. Zamanifar, K. Evolving Fuzzy Neural Network Based Fire Planning in Rescue Firebrigade Agents. SCSC 2006, ISBN 1-56555-307-1.

[3] Takeshi Morimoto. 2002. How to Develop a RoboCupRescue Agent for RoboCupRescue Simulation System version 0. 1st edition.

[4] Kasabov, N. 2001. Evolving Fuzzy Neural Networks for Supervised/ Unsupervised On-line Knowledge-based Learning,. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, vol. 31, no. 6 , 902–918.

DOI: 10.1109/3477.969494

[5] Rabiee, A. GhasemAghaee ,N. A scoring policy for Simulated Soccer Agents Using Reinforcment Learing, ICARA 2004 Conference, NewZeland.

[6] Pedrycz, W., Gomide, F. 1998. An Introduction to Fuzzy Sets: Analysis and Design (complex adaptive systems). MIT Press, Cambridge, MA.

[7] Buckley, J. J., Eslami, E., Esfandiar, E. 2002. An Introduction to Fuzzy Logic and Fuzzy Sets (advances in soft computing),. Physica Verlag.

[8] Jones. T., AT application programming. Charles River Media (2003).

[9] Tom M. Mitchell, Machine Learning, McGraw-Hill (March 1, 1997).

[10] S. J. Russell, P. Norvig, Introduction to AT a modern approach. Prentice Hall.