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2007
Springer

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

8 years 11 months ago
A Probabilistic Relational Model for Characterizing Situations in Dynamic Multi-Agent Systems
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.
Daniel Meyer-Delius, Christian Plagemann, Georg vo
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where GFKL
Authors Daniel Meyer-Delius, Christian Plagemann, Georg von Wichert, Wendelin Feiten, Gisbert Lawitzky, Wolfram Burgard
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