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Authors: Fadwa Sakr and Slim Abdennadher

Affiliation: German University In Cairo, Egypt

ISBN: 978-989-758-172-4

Keyword(s): Multi-agent Planning, Learning, Supervised Learning Algorithms, Classification, RoboCup, Recsue, RoboCup Rescue Simulation, Task Planning.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Bioinformatics ; Biomedical Engineering ; Computational Intelligence ; Cooperation and Coordination ; Distributed and Mobile Software Systems ; Enterprise Information Systems ; Evolutionary Computing ; Formal Methods ; Group Decision Making ; Informatics in Control, Automation and Robotics ; Information Systems Analysis and Specification ; Intelligent Control Systems and Optimization ; Knowledge Discovery and Information Retrieval ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Technologies ; Multi-Agent Systems ; Operational Research ; Planning and Scheduling ; Simulation ; Simulation and Modeling ; Soft Computing ; Software Engineering ; State Space Search ; Symbolic Systems ; Task Planning and Execution

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 p lanning of the agents tasks. (More)

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Paper citation in several formats:
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

@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},
}

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

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