Quintero, CarlosRodríguez, SaithPérez, KatherínLópez, JorgeRojas, EyberthCalderón, Juan2020-01-162020-01-162015-01http://hdl.handle.net/11634/20593This 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.application/pdfAtribución-NoComercial-CompartirIgual 2.5 Colombiahttp://creativecommons.org/licenses/by-nc-sa/2.5/co/Machine learningRoboCup SSLLearning soccer drillsNeural networksSupport vector machineshttps://doi.org/10.1007/978-3-319-18615-3 32Generación de Nuevo Conocimiento: Artículos publicados en revistas especializadas - Electrónicos