Harnessing Supervised Learning Techniques for the Task Planning of Ambulance Rescue Agents

Fadwa Sakr, Slim Abdennadher

2016

Abstract

One of the challenging problems in Artificial Intelligence and Multi-Agent systems is the RoboCup Rescue project that was established in 2001. The Rescue Simulation provides a broad test bench for many algorithms and approaches in the field of AI. The Simulation presents three types of agents: police agents, firebrigade agents and ambulance agents. Each of them has a crucial role in the rescuing problem. The work presented in this paper focuses on the task planning of the ambulance team whose main role is rescuing the maximum number of civilians. It is obvious that this target is a complicated one due to the number of problems that the agent is faced with. One of the problems is estimating the time each civilian takes to die; the Estimated Time of Death (ETD). Realistic estimations of the ETD will lead to a better performance of the ambulance agents by planning their tasks accordingly. Supervised learning is our approach to learn and predict the ETD civilians leading to an optimized planning of the agents tasks.

References

  1. Abouraya, A., Helal, D., Sakr, F., Khater, N., Osama, S., and Abdennadher, S. (2014). Clustering and planning for rescue agent simulation. In RoboCup 2013: Robot World Cup XVII, pages 125-134. Springer.
  2. Afzal, A., Alizadeh, P., Nezhad, M. A., Ghaffari, K., Hasanpour, G., Jahedi, K., Kaviani, P., and Omidvar, G. (2013). Poseidon team description paper robocup 2013, eindhoven.
  3. Bonet, B. and Geffner, H. (2001). Planning as heuristic search. Artificial Intelligence , 129(1):5-33.
  4. Guan, D.-q., Chen, N., and Jiang, Y.-h. (2010). Robocuprescue 2010-rescue simulation league team description. In RoboCup 2010 Symposium Proceeding CD. Singapore: RoboCup Foundation.
  5. Hesam, A., Taheri, P., Ameri, M., Al-Bouye, M., and FaghaniLemraski, M. (2015). Robocup rescue 2015 rescue simulation league team description paper.
  6. Hussein, A., Gervet, C., and Abdennadher, S. (2012). Multi-agent planning for the robocup rescue simulation-applying clustering into task allocation and coordination. In ICAART (2), pages 339-342.
  7. Kitano, H. and Tadokoro, S. (2001). Robocup rescue: A grand challenge for multiagent and intelligent systems. AI magazine, 22(1):39.
  8. Lane, D. M. (2015). Online statistics education: An interactive multimedia course of study.
  9. Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT Press.
  10. Russell, S. and Norvig, P. (1995). Intelligent agents. Artificial intelligence: A modern approach, pages 46-47.
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Paper Citation


in Harvard Style

Sakr F. and Abdennadher S. (2016). Harnessing Supervised Learning Techniques for the Task Planning of Ambulance Rescue Agents . In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-172-4, pages 157-164. DOI: 10.5220/0005692001570164


in Bibtex Style

@conference{icaart16,
author={Fadwa Sakr and Slim Abdennadher},
title={Harnessing Supervised Learning Techniques for the Task Planning of Ambulance Rescue Agents},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2016},
pages={157-164},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005692001570164},
isbn={978-989-758-172-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Harnessing Supervised Learning Techniques for the Task Planning of Ambulance Rescue Agents
SN - 978-989-758-172-4
AU - Sakr F.
AU - Abdennadher S.
PY - 2016
SP - 157
EP - 164
DO - 10.5220/0005692001570164