DocumentCode :
3125857
Title :
Predicting opponent actions in the RoboSoccer
Author :
Ledezma, A. ; Aler, R. ; Sanchis, A. ; Borrajo, D.
Author_Institution :
Departamento de Informatica, Univ. Carlos III de Madrid, Leganes, Spain
Volume :
7
fYear :
2002
fDate :
6-9 Oct. 2002
Abstract :
A 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.
Keywords :
games of skill; learning (artificial intelligence); mobile robots; multi-agent systems; RoboSoccer; hierarchical learning approach; machine learning; multi-agent systems; opponent actions prediction; Brain modeling; Educational robots; Humans; Legged locomotion; Machine learning; Multiagent systems; Predictive models; Sensor phenomena and characterization; Testing; Virtual manufacturing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 2002 IEEE International Conference on
ISSN :
1062-922X
Print_ISBN :
0-7803-7437-1
Type :
conf
DOI :
10.1109/ICSMC.2002.1175692
Filename :
1175692
Link To Document :
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