Analysis and Forecast of Team Formation in the Simulated Robotic Soccer Domain

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2009

Autores

Rui Almeida (Autor) Outra; Luis Paulo Reis (Autor) FEUP; Alipio Mario Jorge (Autor) FCUP

Ata de Conferência Internacional

Título: Progress in Artificial Intelligence: Proceedings of the 14th Portuguese Conference on Artificial Intelligence, EPIA2009

Páginas: 239-250

14th EPIA - Portuguese Conference on Artificial Intelligence - EPIA 2009

Universidade de Aveiro, Portugal, 12 a 15 de Outubro de 2009

Indexação

ISI Web of Knowledge - 12 Citações

Scopus - 15 Citações

Classificação Científica

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

Outras Informações

ID Authenticus: P-003-RVN

DOI: 10.1007/978-3-642-04686-5_20

Abstract (EN): This paper proposes a classification approach to identify the team's formation (formation means the strategical layout of the players in the field) in the robotic soccer domain for the two dimensional (213) simulation league. It is a tool for decision support that allows the coach to understand the strategy of the opponent. To reach that goal we employ Data Mining classification techniques. TO understand the simulated robotic soccer domain we briefly describe the Simulation system, some related work and the use of Data Mining techniques for the detection of formations. In order to perform a robotic soccer match with different formations we develop a way to configure the formations in a training base team (FC Portugal) and a data preparation process. The paper describes the base team and the test team,, used and the respective configuration process. After the matches between test teams the data is subjected to a reduction process taking into account the players' position in the field given the collective. In the modeling stage appropriate learning algorithms were selected. In the solution analysis, the error rate (% incorrectly classify instances) with the statistic test t-Student for paired samples were selected, as the evaluation measure. Experimental results show that it is possible to automatically identify the formations used by the base team (FC Portugal) in distinct matches against different opponents, using Data Mining techniques. The experimental results also show that the SMO (Sequential Minimal Optimization) learning algorithm has the best performance.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12