Abstract
The prey-predator pursuit problem is a generic multi-agent testbed referenced many times in literature. Algorithms and conclusions obtained in this domain can be extended and applied to many particular problems. In first place, greedy algorithms seem to do the job. But when concurrence problems arise, agent communication and coordination is needed to get a reasonable solution. It is quite popular to face these issues directly with non-supervised learning algorithms to train prey and predators. However, results got by most of these approaches still leave a great margin of improvement which should be exploited.
In this paper we propose to start from a greedy strategy and extend and improve it by adding communication and machine learning. In this proposal, predator agents get a previous movement decision by using a greedy approach. Then, they focus on learning how to coordinate their own pre-decisions with the ones taken by other surrounding agents. Finally, they get a final decission trying to optimize their chase of the prey without colliding between them. For the learning step, a neuroevolution approach is used. The final results show improvements and leave room for open discussion.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Benda, M., Jagannathan, V., Dodhiawalla, R.: On optimal cooperation of knowledge sources. Technical Report Tech. Rep. BCS-G2010-28, Boeing AI Center, Boeing Computer Services, Bellevue, WA (1986)
Chainbi, W., Hanachi, C., Sibertin-Blanc, C.: The Multi-agent Prey-Predator problem: A Petri net solution. In: Proceedings of the IMACS-IEEE-SMC conference on Computational Engineering in Systems Application (CESA 1996), Lille, France, pp. 692–697 (1996)
Haynes, T., Sen, S.: Evolving behavioral strategies in predators and prey. In: Sen, S. (ed.) IJCAI 1995 Workshop on Adaptation and Learning in Multiagent Systems, Montreal, Quebec, Canada, pp. 32–37. Morgan Kaufmann, San Francisco (1995)
Jim, K.-C., Giles, C.L.: Talking helps: Evolving communicating agents for the predator-prey pursuit problem. Artificial Life 6(3), 237–254 (2000)
Katayama, K., Koshiishi, T., Narihisa, H.: Reinforcement learning agents with primary knowledge designed by analytic hierarchy process. In: SAC 2005: Proceedings of the 2005 ACM symposium on Applied computing, pp. 14–21. ACM, New York (2005)
Kok, J.R., Vlassis, N.: The pursuit domain package. Technical Report Technical Report IAS-UVA-03-03, Informatics Institute, University of Amsterdam, The Netherlands (August 2003)
Korf, R.E.: A simple solution to pursuit games. In: Proceedings of the 11th International Workshop on Distributed Artificial Intelligence, Glen Arbor, MI, pp. 183–194 (February 1992)
Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evolutionary Computation 10(2), 99–127 (2002)
Stone, P., Veloso, M.M.: Multiagent systems: A survey from a machine learning perspective. Autonomous Robots 8(3), 345–383 (2000)
Tan, M.: Multi-agent reinforcement learning: Independent vs. cooperative learning. In: Huhns, M.N., Singh, M.P. (eds.) Readings in Agents, pp. 487–494. Morgan Kaufmann, San Francisco (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Reverte, J., Gallego, F., Satorre, R., Llorens, F. (2008). Mixing Greedy and Evolutive Approaches to Improve Pursuit Strategies. In: Geffner, H., Prada, R., Machado Alexandre, I., David, N. (eds) Advances in Artificial Intelligence – IBERAMIA 2008. IBERAMIA 2008. Lecture Notes in Computer Science(), vol 5290. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88309-8_21
Download citation
DOI: https://doi.org/10.1007/978-3-540-88309-8_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88308-1
Online ISBN: 978-3-540-88309-8
eBook Packages: Computer ScienceComputer Science (R0)