Behavior classification with Self-Organizing Maps

Michael Wünstel; Daniel Polani; Thomas Uthmann; Jürgen Perl

doi:10.1007/3-540-45324-5_9

Behavior classification with Self-Organizing Maps

Michael Wünstel, Daniel Polani, Thomas Uthmann, Jürgen Perl

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

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 language	English
Title of host publication	RoboCup 2000
Subtitle of host publication	Robot Soccer World Cup IV
Pages	108-118
Number of pages	11
DOIs	https://doi.org/10.1007/3-540-45324-5_9
Publication status	Published - 20 Sept 2001
Event	4th Robot World Cup Soccer Games and Conferences, RoboCup 2000 - Melbourne, VIC, Australia Duration: 27 Aug 2000 → 3 Sept 2000

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2019 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th Robot World Cup Soccer Games and Conferences, RoboCup 2000
Country/Territory	Australia
City	Melbourne, VIC
Period	27/08/00 → 3/09/00

Access to Document

10.1007/3-540-45324-5_9Licence: Other

Cite this

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title = "Behavior classification with Self-Organizing Maps",

abstract = "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.",

author = "Michael W{\"u}nstel and Daniel Polani and Thomas Uthmann and J{\"u}rgen Perl",

note = "{\textcopyright} Springer-Verlag Berlin Heidelberg 2001; 4th Robot World Cup Soccer Games and Conferences, RoboCup 2000 ; Conference date: 27-08-2000 Through 03-09-2000",

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Wünstel, M, Polani, D, Uthmann, T & Perl, J 2001, Behavior classification with Self-Organizing Maps. in RoboCup 2000: Robot Soccer World Cup IV. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2019 LNAI, pp. 108-118, 4th Robot World Cup Soccer Games and Conferences, RoboCup 2000, Melbourne, VIC, Australia, 27/08/00. https://doi.org/10.1007/3-540-45324-5_9

Behavior classification with Self-Organizing Maps. / Wünstel, Michael; Polani, Daniel; Uthmann, Thomas et al.
RoboCup 2000: Robot Soccer World Cup IV. 2001. p. 108-118 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2019 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Polani, Daniel

AU - Uthmann, Thomas

AU - Perl, Jürgen

PY - 2001/9/20

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N2 - 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.

AB - 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.

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DO - 10.1007/3-540-45324-5_9

M3 - Conference contribution

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Y2 - 27 August 2000 through 3 September 2000

ER -

Behavior classification with Self-Organizing Maps

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