Title :
Study on shooting skill in RoboCup Simulator League
Author :
Yang, Zeng-Guang ; Li, Long-Shu ; Yue, Yi ; Tang, Bin
Author_Institution :
Key Lab of IC&SP, Anhui Univ., Hefei, China
Abstract :
This paper describes a training method based on decision tree for shooting skill of the agents in the RoboCup simulator league. The training method enables an agent to find the best shooting point and shooting time to get the maximum probability of scoring.
Keywords :
decision trees; digital simulation; probability; robots; software agents; training; RoboCup simulator league; agent training method; decision tree; scoring maximum probability; shoot evaluating ability; shooting skill; shooting time; state predicting ability; Artificial intelligence; Computational modeling; Computer science; Computer science education; Computer simulation; Decision trees; Machine learning; Predictive models; Robots; State-space methods;
Conference_Titel :
Machine Learning and Cybernetics, 2003 International Conference on
Print_ISBN :
0-7803-8131-9
DOI :
10.1109/ICMLC.2003.1259849
