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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Oliveira, R. Adeodato, P. Carvalho, A. Viegas, I. Diego, C. Tsang Ing-Ren |
| Copyright Year | 2009 |
| Description | Author affiliation: Center of Informatics, Federal University of Pernambuco, Brazil (Oliveira, R.; Adeodato, P.; Carvalho, A.; Viegas, I.; Diego, C.; Tsang Ing-Ren) |
| Abstract | In soccer, scoring goals is a fundamental objective which depends on many conditions and constraints. Considering the RoboCup soccer 2D-simulator, this paper presents a data mining-based decision system to identify the best time and direction to kick the ball towards the goal to maximize the overall chances of scoring during a simulated soccer match. Following the CRISP-DM methodology, data for modeling were extracted from matches of major international tournaments (10691 kicks), knowledge about soccer was embedded via transformation of variables and a Multilayer Perceptron was used to estimate the scoring chance. Experimental performance assessment to compare this approach against previous LDA-based approach was conducted from 100 matches. Several statistical metrics were used to analyze the performance of the system and the results showed an increase of 7.7% in the number of kicks, producing an overall increase of 78% in the number of goals scored. |
| Starting Page | 2347 |
| Ending Page | 2352 |
| File Size | 917441 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424435487 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2009.5178616 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-06-14 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Data mining Neural networks Multilayer perceptrons Service robots Machine learning Machine learning algorithms Linear discriminant analysis Performance analysis Helium Artificial intelligence |
| Content Type | Text |
| Resource Type | Article |
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