A Knowledge-Based Approach for Automatic Generation of Summaries of Behavior

  • Martin Molina
  • Victor Flores
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4183)


Effective automatic summarization usually requires simulating human reasoning such as abstraction or relevance reasoning. In this paper we describe a solution for this type of reasoning in the particular case of surveillance of the behavior of a dynamic system using sensor data. The paper first presents the approach describing the required type of knowledge with a possible representation. This includes knowledge about the system structure, behavior, interpretation and saliency. Then, the paper shows the inference algorithm to produce a summarization tree based on the exploitation of the physical characteristics of the system. The paper illustrates how the method is used in the context of automatic generation of summaries of behavior in an application for basin surveillance in the presence of river floods.


Automatic Generation Management Goal Aggregation Rule Inference Algorithm Interpretation Model 
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 2006

Authors and Affiliations

  • Martin Molina
    • 1
  • Victor Flores
    • 1
  1. 1.Department of Artificial IntelligenceUniversidad Politécnica de MadridBoadilla del Monte, MadridSpain

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