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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2014

Autores

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

Revista

Título: Applied Soft Computing Journal

Vol. 23

Páginas: 180-193

ISSN: 1568-4946

Editora: Elsevier

Indexação

ISI Web of Knowledge - 9 Citações

ISI Web of Science

Scopus - 11 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

CORDIS: Ciências Físicas > Ciência de computadores > Cibernética > Inteligência artificial

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14