Towards automated team analysis: a machine learning approach

Abstract:

Teamwork is becoming increasingly important in a large number of multiagent applications. With the growing importance of teamwork, there is now an increasing need for tools for analysis and evaluation of such teams. We are developing automated techniques for analyzing agent teams. In this paper we present ISAAC, an automated assistant that uses these techniques to perform post-hoc analysis of RoboCup teams. ISAAC requires little domain knowledge, instead using data mining and inductive learning tools to produce the analysis. ISAAC has been applied to all of the teams from RoboCup’97, RoboCup’98, and Pricai’98 in a fully automated fashion. Furthermore, ISAAC is available online for use by developers of RoboCup teams.