Sequential pattern mining for situation and behavior prediction in simulated robotic soccer

Andreas D. Lattner, Andrea Miene, Ubbo Visser, Otthein Herzog

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

33 Scopus citations


Agents in dynamic environments have to deal with world representations that change over time. In order to allow agents to act autonomously and to make their decisions on a solid basis an interpretation of the current scene is necessary. If intentions of other agents or events that are likely to happen in the future can be recognized the agent's performance can be improved as it can adapt the behavior to the situation. In this work we present an approach which applies unsupervised symbolic learning off-line to a qualitative abstraction in order to create frequent patterns in dynamic scenes. These patterns can be later applied during runtime in order to predict future situations and behaviors. The pattern mining approach was applied to two games of the 2D RoboCup simulation league.

Original languageEnglish (US)
Title of host publicationRoboCup 2005
Subtitle of host publicationRobot Soccer World Cup IX
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783540354376
StatePublished - Jan 1 2006
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4020 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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