Mixing greedy and evolutive approaches to improve pursuit strategies
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Título: | Mixing greedy and evolutive approaches to improve pursuit strategies |
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Autor/es: | Reverte Bernabeu, Juan | Gallego-Durán, Francisco J. | Satorre Cuerda, Rosana | Llorens Largo, Faraón |
Grupo/s de investigación o GITE: | Informática Industrial e Inteligencia Artificial |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial |
Palabras clave: | Multi-agent systems | Communication | Coordination | Neuroevolution |
Área/s de conocimiento: | Ciencia de la Computación e Inteligencia Artificial |
Fecha de creación: | 14-oct-2008 |
Fecha de publicación: | 14-oct-2008 |
Editor: | Springer Berlin / Heidelberg |
Cita bibliográfica: | REVERTE BERNABEU, Juan, et al. "Mixing greedy and evolutive approaches to improve pursuit strategies". En: Advances in Artificial Intelligence, IBERAMIA 2008 : 11th Ibero-American Conference on AI, Lisbon, Portugal, October 14-17, 2008 : Proceedings / Hector Geffner [et al.] (Eds.). Berlin : Springer, 2008. (Lecture Notes in Computer Science; Vol. 5290). ISBN 978-3-540-88308-1, pp. 203-212 |
Resumen: | 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. |
URI: | http://hdl.handle.net/10045/8385 |
ISBN: | 978-3-540-88308-1 |
ISSN: | 0302-9743 (Print) | 1611-3349 (Online) |
DOI: | 10.1007/978-3-540-88309-8_21 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/bookPart |
Derechos: | The original publication is available at www.springerlink.com |
Revisión científica: | si |
Versión del editor: | http://dx.doi.org/10.1007/978-3-540-88309-8_21 |
Aparece en las colecciones: | INV - i3a - Comunicaciones a Congresos, Conferencias, etc. INV - Smart Learning - Comunicaciones a Congresos, Conferencias, etc. |
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reverte08mixing.pdf | Versión revisada (acceso libre) | 148,07 kB | Adobe PDF | Abrir Vista previa |
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