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.

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