@INPROCEEDINGS{Lattnerg,
  author = {Lattner, Andreas D. and Herzog, Otthein},
  title = {Mining Temporal Patterns from Relational Data},
  booktitle = {LWA 2005, Lernen Wissensentdeckung Adaptivit{\"a}t},
  year = {2005},
  pages = {184--189},
  month = {October1--2},
  abstract = {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 temporal patterns in dynamic scenes.
	Here, an adaption of a sequential pattern mining algorithm which
	was presented earlier by the authors is proposed in order to reduce
	the complexity by handling different aspects (class restrictions,
	variable unifications, and temporal relations) separatedly first,
	and then combining the results of the single steps. The work is still
	in progress-- this paper introduces the basic ideas and shows an
	example run of the implemented system.},
  owner = {pmania},
  timestamp = {2012.11.06},
  url = {http://www-agki.tzi.de/grp/ag-ki/download/2005/lattnerHerzog05fgml.pdf}
}