Improving a simulated soccer team's performance through a Memory-Based Collaborative Filtering approach

• Type: Article in International Scientific Journal
• Date: 2014

Authors

Pedro Henriques Abreu (Author) Other; Daniel Castro Silva (Author) FEUP; Fernando Almeida (Author) Other; João Mendes-Moreira (Author) FEUP

Journal

Title: Applied Soft Computing Journal

Vol. 23

Pages: 180-193

ISSN: 1568-4946

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 9 Citations

ISI Web of Science

Scopus - 11 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

Other information

Authenticus ID: P-009-NXH

DOI: 10.1016/j.asoc.2014.06.021

Abstract (EN): Collaborative filtering techniques have been used for some years, almost exclusively in Internet environments, helping users find items they are expected to like by using the user's past purchases to provide such recommendations. With this concept in mind, this research uses a collaborative filtering technique to automatically improve the performance of a simulated soccer team. Many studies have attempted to address this problem over the last years but none has shown meaningful improvements in the performance of the soccer team. Using a collaborative filtering technique based on nearest neighbors and the FC Portugal team as the test subject (in the context of the RoboCup 2D Simulation League), several simulations were run for matches against different teams with much better, better and worse performance than FC Portugal. The strategy used by FC Portugal was to combine 8 set-plays and 2 team formations. The simulation results revealed an improvement in performance between 32% and 384%. In the future, there are plans to expand this approach to other contexts, such as the 3D Simulation League.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14