Robot Soccer Strategy Reduction by Representatives

Abstract

The robot soccer game introduces a variable and dynamic environment for cooperating agents. Coverage of areas such as multi-agent systems, robot control, optimal path planning, real-time image processing and machine learning makes this domain very attractive. This article presents our approach to strategy description of the robot soccer game and a method of real-time strategy adaptation performed during the game. The real-time strategy adaptation method improves the strategy by adding new rules to it. During this process many new rules can be added to the original strategy, thus making it more robust but more difficult to manage. Therefore, this article presents our method for strategy reduction using representatives, in terms of the number of rules within the strategy, while preserving the quality of the adapted strategy. Strategy, as we defined it, describes a space from the real world in which we know the physical coordinates of objects located in it. Therefore, the methods we developed for strategy planning can be applied to it.