Name
Title | ||
|---|---|---|
Using model-based collaborative filtering techniques to recommend the expected best strategy to defeat a simulated soccer opponent |
Abstract | ||
|---|---|---|
How to improve the performance of a simulated soccer team using final game statistics? This is the question this research aims to answer using model-based collaborative techniques and a robotic team FC Portugal as a case study. After developing a framework capable of automatically calculating the final game statistics through the RoboCup log files, a feature selection algorithm was used to select the variables that most influence the final game result. In the next stage, given the statistics of the current game, we rank the strategies that obtained the maximum average of goal difference in similar past games. This is done by splitting offline past games into different k-clusters. Then, for each cluster, the expected best strategy was assigned. The online phase consists in the selection of the expected best strategy for the cluster in which the current game best fits. Regarding the final results, our approach proved that it is possible to improve the performance of a robotic team by more than 35%, even in a competitive environment such as the RoboCup 2D simulation league. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.3233/IDA-140678 | Intell. Data Anal. |
Keywords | Field | DocType |
collaborative filtering,model-based techniques,robotic soccer simulation,support vector machines,clustering | Collaborative filtering,Feature selection,Computer science,Support vector machine,Artificial intelligence,Adversary,Cluster analysis,Machine learning | Journal |
Volume | Issue | ISSN |
18 | 5 | 1088-467X |
Citations | PageRank | References |
3 | 0.36 | 27 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro Abreu | 1 | 92 | 19.58 |
| Daniel Castro Silva | 2 | 51 | 13.31 |
| João Portela | 3 | 3 | 0.36 |
| João Mendes-Moreira | 4 | 317 | 29.50 |
| Luís Paulo Reis | 5 | 482 | 83.34 |