Abstract
Recognizing plans of moving agents is a natural goal for many sensor systems, with applications including robotic pathfinding, traffic control, and detection of anomalous behavior. This paper considers plan recognition complicated by the absence of contextual information such as labeled plans and relevant locations. Instead, we introduce 2 unsupervised methods to simultaneously estimate model parameters and hidden values within a Factor graph representing agent transitions over time. We evaluate our approach by applying it to goal prediction in a GPS dataset tracking 1074 ships over 5 days in the English channel.
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Davis, G.B., Olson, J., Carley, K.M. (2010). Unsupervised Plan Detection with Factor Graphs. In: Gaber, M.M., Vatsavai, R.R., Omitaomu, O.A., Gama, J., Chawla, N.V., Ganguly, A.R. (eds) Knowledge Discovery from Sensor Data. Sensor-KDD 2008. Lecture Notes in Computer Science, vol 5840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12519-5_4
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DOI: https://doi.org/10.1007/978-3-642-12519-5_4
Publisher Name: Springer, Berlin, Heidelberg
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