Ledezma Espino, Agapito IsmaelAler, RicardoSanchis de Miguel, María AraceliBorrajo Millán, Daniel2009-12-182009-12-182002-10IEEE International Conference on Systems, Man and Cybernetics, 2002, vol. 70-7803-7437-11062-922Xhttps://hdl.handle.net/10016/6159Proceeding of: IEEE International Conference on Systems, Man, and Cybernetics (SMC-2002), 6-9 Oct. 2002, Hammamet, TunezA very important issue in multi-agent systems is that of adaptability to other agents, be it to cooperate or to compete. In competitive domains, the knowledge about the opponent can give any player a clear advantage. In previous work, we acquired models of another agent (the opponent) based only on the observation of its inputs and outputs (its behavior) by formulating the problem as a classification task. In this paper we extend this previous work to the RoboCup domain. However, we have found that models based on a single classifier have bad accuracy, To solve this problem, In this paper we propose to decompose the learning task into two tasks: learning the action name (i.e. kick or dash) and learning the parameter of that action. By using this hierarchical learning approach accuracy results improve, and at worst, the agent can know what action the opponent will carry out, even if there is no high accuracy on the action parameter.application/pdfeng© IEEEGames of skillLearning (artificial intelligence)Mobile robotsMulti-agent systemsRoboSoccerMachine learningconference paperInformática10.1109/ICSMC.2002.1175692open accessIEEE International Conference on Systems, Man and Cybernetics7