Using a Two-Layered Case-Based Reasoning for Prediction in Soccer Coach.

Abstract

Abstract — The prediction of the future states in Multi-Agent Systems has been a challenging problem since the begining of MAS. Robotic soccer is a MAS environment in which the predictions of the opponents strategy, or opponent modeling, plays an important role. In this paper, a novel case-based architecture is applied in the soccer coach that learns and predicts opponent movements. Case-Based Reasoning(CBR) is a powerful and a frequently applied way to solve problems for humans. However, using CBR in highly dynamic environments results in a large number of cases to be retained, leading to high computational costs for subsequent case management(selection, composition and adaptation). The novel two-layered CBR learning architecture consists of an additional layer of cases to an ordinary layer which provides representation, adaptation and similarity measurement parameters for it. In this way, the complexity of the problem will go back to two subproblems, each of which works with a relatively small number of cases, so preventing the mentioned side effects of an ordinary CBR. We will provide and compare the performance statistics of both the ordinary and the two-layerd CBR learning systems and discuss features of this domain that makes the proposed learning approach perform well.