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A Probabilistic Relational Model for Characterizing Situations in Dynamic Multi-Agent Systems

  • Daniel Meyer-Delius
  • Christian Plagemann
  • Georg von Wichert
  • Wendelin Feiten
  • Gisbert Lawitzky
  • Wolfram Burgard
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Artificial systems with a high degree of autonomy require reliable semantic information about the context they operate in. State interpretation, however, is a difficult task. Interpretations may depend on a history of states and there may be more than one valid interpretation. We propose a model for spatio-temporal situations using hidden Markov models based on relational state descriptions, which are extracted from the estimated state of an underlying dynamic system. Our model covers concurrent situations, scenarios with multiple agents, and situations of varying durations. To evaluate the practical usefulness of our model, we apply it to the concrete task of online traffic analysis.

Keywords

Hide Markov Model Abstract State Situation Model Observation Sequence Relational Description 
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

  • Daniel Meyer-Delius
    • 1
  • Christian Plagemann
    • 1
  • Georg von Wichert
    • 2
  • Wendelin Feiten
    • 2
  • Gisbert Lawitzky
    • 2
  • Wolfram Burgard
    • 1
  1. 1.Department for Computer ScienceUniversity of FreiburgGermany
  2. 2.Information and CommunicationsSiemens Corporate TechnologyGermany

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