Behavior classification with Self-Organizing Maps

Michael Wünstel, Daniel Polani, Thomas Uthmann, Jürgen Perl

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)


We describe a method that applies Self-Organizing Maps for direct clustering of spatio-temporal data. We use the method to evaluate the behavior of RoboCup players. By training the Self-Organizing Map with player data we have the possibility to identify various clusters representing typical agent behavior patterns. Thus we can draw certain conclusions about their tactical behavior, using purely motion data, i.e. logfile information. In addition, we examine the player-ball interaction that give information about the players' technical capabilities.
Original languageEnglish
Title of host publicationRoboCup 2000
Subtitle of host publicationRobot Soccer World Cup IV
Number of pages11
Publication statusPublished - 20 Sept 2001
Event4th Robot World Cup Soccer Games and Conferences, RoboCup 2000 - Melbourne, VIC, Australia
Duration: 27 Aug 20003 Sept 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2019 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference4th Robot World Cup Soccer Games and Conferences, RoboCup 2000
CityMelbourne, VIC


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