Learning soccer drills for the small size league of RoboCup

dc.contributor.authorQuintero, Carlos
dc.contributor.authorRodríguez, Saith
dc.contributor.authorPérez, Katherín
dc.contributor.authorLópez, Jorge
dc.contributor.authorRojas, Eyberth
dc.contributor.authorCalderón, Juan
dc.date.accessioned2020-01-16T21:14:38Z
dc.date.available2020-01-16T21:14:38Z
dc.date.issued2015-01
dc.description.abstractThis paper shows the results of applying machine learning techniques to the problem of predicting soccer plays in the Small Size League of RoboCup. We have modeled the task as a multi-class classification problem by learning the plays of the STOx’s team. For this, we have created a database of observations for this team’s plays and obtained key features that describe the game state during a match. We have shown experimentally, that these features allow two learning classifiers to obtain high prediction accuracies and that most miss-classified observations are found early on the plays.spa
dc.description.domainhttp://unidadinvestigacion.usta.edu.cospa
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1007/978-3-319-18615-3 32spa
dc.identifier.urihttp://hdl.handle.net/11634/20593
dc.publisher.branchCRAI-USTA Bogotáspa
dc.relation.referencesSalustowicz, R.P., Wiering, M.A., Schmidhuber, J.: Learning team strategies: soccer case studies. Mach. Learn. 33, 263–282 (1998)spa
dc.relation.referencesStone, P.: Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer. MIT Press, United States (2000)spa
dc.relation.referencesBianchi, R. A., Ribeiro, C. H., Costa, A. H.: Heuristic selection of actions in multiagent reinforcement learning. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 690–696. Morgan Kaufmann Publishers Inc., San Francisco (2007)spa
dc.relation.referencesStone, Peter, Kuhlmann, Gregory, Taylor, Matthew E., Liu, Yaxin: Keepaway soccer: from machine learning testbed to benchmark. In: Bredenfeld, Ansgar, Jacoff, Adam, Noda, Itsuki, Takahashi, Yasutake (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020, pp. 93–105. Springer, Heidelberg (2006)spa
dc.relation.referencesDuan, Y., Liu, Q., Xu, X.: Application of reinforcement learning in robot soccer. Eng. Appl. Artif. Intell. 20, 936–950 (2007)spa
dc.relation.referencesKarol, A., Nebel, B., Stanton, C., Williams, M.-A.: Case based game play in the RoboCup four-legged league. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS (LNAI), vol. 3020, pp. 739–747. Springer, Heidelberg (2004)spa
dc.relation.referencesMarling, C., Tomko, M., Gillen, M., Alexander, D., Chelberg, D.: Case-based reasoning for planning and world modeling in the RoboCup small size league. In: IJCAI Workshop on Issues in Designing Physical Agents for Dynamic Real-time Environments, Acapulco (2003)spa
dc.relation.referencesWendler, J., Bach, J.: Recognizing and predicting agent behavior with case based reasoning. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS (LNAI), vol. 3020, pp. 729–738. Springer, Heidelberg (2004)spa
dc.relation.referencesRos, R., L´opez de M`antaras, R., Arcos, J.-L., Veloso, M.M.: Team playing behavior in robot soccer: a case-based reasoning approach. In: Weber, R.O., Richter, M.M. (eds.) ICCBR 2007. LNCS (LNAI), vol. 4626, pp. 46–60. Springer, Heidelberg (2007)spa
dc.relation.referencesSteffens, T.: Adapting similarity-measures to agent types in opponent modelling. In: Workshop on Modeling Other Agents from Observations at AAMA, pp. 125– 128, New York (2004)spa
dc.relation.referencesAhmadi, M., Lamjiri, A.K., Nevisi, M.M., Habibi, J., Badie, K.: Using two-layered case-based reasoning for prediction in soccer coach. In: International Conference of Machine Learning. Models, Technologies and Applications, pp. 181–185. CSREA Press, Las Vegas (2003)spa
dc.relation.referencesLattner, A.D., Miene, A., Visser, U., Herzog, O.: Sequential pattern mining for situation and behavior prediction in simulated robotic soccer. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020, pp. 118–129. Springer, Heidelberg (2006)spa
dc.relation.referencesRiley, P., Veloso, M.: On behavior classification in adversarial environments. In: Parker, L.E., Bekey, G., Barhen, J. (eds.) Distributed Autonomous Robotic Systems, pp. 371–380. Springer, Tokyo (2000)spa
dc.relation.referencesVisser, U., Drcker, C., Hbner, S., Schmidt, E., Weland, H.: Sequential pattern mining for situation and behavior prediction in simulated robotic soccer. In: Stone, P., Balch, T., Kraetzschmar, G.K. (eds.) RoboCup 2000. LNCS (LNAI), vol. 2019, pp. 391–396. Springer, Heidelberg (2001)spa
dc.relation.referencesFaria, B.M., Reis, L.P., Lau, N., Castillo, G.: Machine learning algorithms applied to the classification of robotic soccer formations and opponent teams. In: 2010 IEEE Conference on Cybernetics and Intelligent Systems, pp. 344–349. IEEE Press, Singapore (2010)spa
dc.relation.referencesTrevizan, F., Veloso, M.: Learning opponent’s strategies in the RoboCup small size league. In: Proceedings of the AAMAS 2010 Workshop on Agents in Real-time and Dynamic Environments, pp. 45–52, Toronto (2010)spa
dc.relation.referencesKonur, S., Ferrein, A., Lakemeyer, G.: Learning decision trees for action selection in soccer agents. In: Proceedings of the ECAI-04 Workshop on Agents in Dynamic and Real-time Environments. IOS Press, Valencia (2004)spa
dc.relation.referencesLedezma, A., Aler, R., Sanch´ıs, A., Borrajo, D.: Predicting opponent actions by observation. In: Nardi, D., Riedmiller, M., Sammut, C., Santos-Victor, J. (eds.) RoboCup 2004. LNCS (LNAI), vol. 3276, pp. 286–296. Springer, Heidelberg (2005)spa
dc.relation.referencesChang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Tech. 2, 1–27 (2011). http://www.csie.ntu.edu.tw/∼ cjlin/libsvmspa
dc.relation.referencesSTOx’s team webpage. http://www.stoxs.org/index.php/en/projects/robocupssl-2spa
dc.rightsAtribución-NoComercial-CompartirIgual 2.5 Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/2.5/co/
dc.subject.keywordMachine learningspa
dc.subject.keywordRoboCup SSLspa
dc.subject.keywordLearning soccer drillsspa
dc.subject.keywordNeural networksspa
dc.subject.keywordSupport vector machinesspa
dc.titleLearning soccer drills for the small size league of RoboCupspa
dc.type.categoryGeneración de Nuevo Conocimiento: Artículos publicados en revistas especializadas - Electrónicosspa

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
Learning soccer drills for the small size league of RoboCup.pdf
Tamaño:
786.84 KB
Formato:
Adobe Portable Document Format
Descripción:
Artículo SCOPUS

Bloque de licencias

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
license.txt
Tamaño:
807 B
Formato:
Item-specific license agreed upon to submission
Descripción: