Efficient Graph Matching with Application to Cognitive Automation

  • Alexander Matzner
  • Mark Minas
  • Axel Schulte
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5088)


Cognitive automation has proven to be an applicable approach to handle increasing complexity in automation. Although fielded prototypes have already been demonstrated, the real time performance of the underlying software framework COSA is currently a limiting factor with respect to a further increase of the application complexity. In this paper we describe a cognitive framework with increased performance for the use in cognitive systems for vehicle guidance automation tasks. It uses a combination of several existing graph transformation algorithms and techniques. We show, that for our approach, the incremental rule matching that we propose yields a performance gain over the non-incremental algorithm and a large increase over the existing generic cognitive framework COSA for a typical application.


Graph Transformation Graph Grammar Rule Match Instance Graph Pattern Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Alexander Matzner
    • 1
  • Mark Minas
    • 2
  • Axel Schulte
    • 1
  1. 1.Institute for Flight Dynamics & Flight GuidanceGermany
  2. 2.Institute for Software TechnologyUniversität der Bundeswehr MünchenGermany

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