Abstract
Although various methods have already been utilised in the RoboCup Rescue simulation project, we investigated a new approach and implemented self-organising agents without any central instance. Coordinated behaviour is achieved by using a task allocation system. The task allocation system supports an adjustable evaluation function, which gives the agents options on their behaviour. Weights for each evaluation function were evolved using an evolutionary algorithm. We additionally investigated different settings for the learning algorithm. We gained extraordinary high scores on deterministic simulation runs with reasonable acting agents.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
RoboCup Rescue Project, http://www.robocuprescue.org
PaderRescue: Project-group for Agent-based Disaster Management and Emergent Realtime Rescue for Emergency SCenarios in Uncertain Environments, University of Paderborn, http://wwwcs.uni-paderborn.de/cs/ag-klbue/de/research/PaderRescue/pgrescue.html
Stef, B.M.P., Maurits, L.F., Visser, A.: The High-Level Communication Model for Multi-agent Coordination in the RoboCupRescue Simulator. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS (LNAI), vol. 3020, pp. 503–509. Springer, Heidelberg (2004)
Ohta, M.: An Implementation of Rescue Agents with Genetic Algorithm. In: 2nd International Workshop on Synthetic Simulation and Robotics to Mitigate Earthquake Disaster, Lisbon (2004), http://www.rescuesystem.org/robocuprescue/SRMED2004/MOhta.pdf
Paquet, S., Bernier, N., Chaib-draa, B.: Comparison of Different Coordination Strategies for the RoboCupRescue Simulation. In: Orchard, B., Yang, C., Ali, M. (eds.) IEA/AIE 2004. LNCS (LNAI), vol. 3029, pp. 987–996. Springer, Heidelberg (2004)
Nair, R., Ito, T., Tambe, M., Marsella, S.: Task Allocation in the RoboCup Rescue Simulation Domain: A Short Note. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS (LNAI), vol. 2377, pp. 751–754. Springer, Heidelberg (2002)
Eiben, A., Smith, J.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kruse, M. et al. (2009). Learning Parametrised RoboCup Rescue Agent Behaviour Using an Evolutionary Algorithm. In: Mertsching, B., Hund, M., Aziz, Z. (eds) KI 2009: Advances in Artificial Intelligence. KI 2009. Lecture Notes in Computer Science(), vol 5803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04617-9_81
Download citation
DOI: https://doi.org/10.1007/978-3-642-04617-9_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04616-2
Online ISBN: 978-3-642-04617-9
eBook Packages: Computer ScienceComputer Science (R0)