A data mining approach in opponent modeling
In offline opponent modeling, large datasets can be utilized as training data to model the opponent. In the Coach competition of RoboCup Soccer, offline opponent modeling can be adopted to train the coach learn about the opponent's behavior patterns. Data-mining techniques, particularly decisio...
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2005
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oai:animorepository.dlsu.edu.ph:faculty_research-14972021-12-13T08:21:22Z A data mining approach in opponent modeling Bulos, Remedios De Dios Dulalia, Conirose Go, Peggy Sharon L. Tan, Pamela Vianne C. Uy, Ma Zaide Ilene O. In offline opponent modeling, large datasets can be utilized as training data to model the opponent. In the Coach competition of RoboCup Soccer, offline opponent modeling can be adopted to train the coach learn about the opponent's behavior patterns. Data-mining techniques, particularly decision-tree construction can be applied in identifying interesting behavior patterns of the opponent. This research explores the use of the decision-tree algorithm C4.5 to generate classification rules that will embody the offensive and defensive strategies (plans) of the coach against its opponent(s). To achieve this objective, the SimSoccer Coach system is built. © Springer-Verlag Berlin Heidelberg 2005. 2005-01-01T08:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/498 https://animorepository.dlsu.edu.ph/context/faculty_research/article/1497/type/native/viewcontent Faculty Research Work Animo Repository Data mining Soccer—Defense Soccer—Training Computer Sciences |
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Data mining Soccer—Defense Soccer—Training Computer Sciences |
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Data mining Soccer—Defense Soccer—Training Computer Sciences Bulos, Remedios De Dios Dulalia, Conirose Go, Peggy Sharon L. Tan, Pamela Vianne C. Uy, Ma Zaide Ilene O. A data mining approach in opponent modeling |
| description |
In offline opponent modeling, large datasets can be utilized as training data to model the opponent. In the Coach competition of RoboCup Soccer, offline opponent modeling can be adopted to train the coach learn about the opponent's behavior patterns. Data-mining techniques, particularly decision-tree construction can be applied in identifying interesting behavior patterns of the opponent. This research explores the use of the decision-tree algorithm C4.5 to generate classification rules that will embody the offensive and defensive strategies (plans) of the coach against its opponent(s). To achieve this objective, the SimSoccer Coach system is built. © Springer-Verlag Berlin Heidelberg 2005. |
| format |
text |
| author |
Bulos, Remedios De Dios Dulalia, Conirose Go, Peggy Sharon L. Tan, Pamela Vianne C. Uy, Ma Zaide Ilene O. |
| author_facet |
Bulos, Remedios De Dios Dulalia, Conirose Go, Peggy Sharon L. Tan, Pamela Vianne C. Uy, Ma Zaide Ilene O. |
| author_sort |
Bulos, Remedios De Dios |
| title |
A data mining approach in opponent modeling |
| title_short |
A data mining approach in opponent modeling |
| title_full |
A data mining approach in opponent modeling |
| title_fullStr |
A data mining approach in opponent modeling |
| title_full_unstemmed |
A data mining approach in opponent modeling |
| title_sort |
data mining approach in opponent modeling |
| publisher |
Animo Repository |
| publishDate |
2005 |
| url |
https://animorepository.dlsu.edu.ph/faculty_research/498 https://animorepository.dlsu.edu.ph/context/faculty_research/article/1497/type/native/viewcontent |
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