Selecting the Best Player Formation for Corner-Kick Situations Based on Bayes' Estimation
This work addresses the specific challenge of improving team performance in a simulated soccer domain, representing an incremental advancement in strategy selection for robotic soccer.
The paper tackles the problem of selecting optimal player formations for corner-kick situations in the RoboCup Soccer simulation 2D league by proposing a model that uses sequential Bayes' estimators to determine the most effective strategies against clusters of opponent teams, showing satisfying abilities to rank similar formations with a decent number of simulation games.
In the domain of the Soccer simulation 2D league of the RoboCup project, appropriate player positioning against a given opponent team is an important factor of soccer team performance. This work proposes a model which decides the strategy that should be applied regarding a particular opponent team. This task can be realized by applying preliminary a learning phase where the model determines the most effective strategies against clusters of opponent teams. The model determines the best strategies by using sequential Bayes' estimators. As a first trial of the system, the proposed model is used to determine the association of player formations against opponent teams in the particular situation of corner-kick. The implemented model shows satisfying abilities to compare player formations that are similar to each other in terms of performance and determines the right ranking even by running a decent number of simulation games.