Prediction Sharing Across Time and Contexts

Oskar Dressler, Hartmut Freitag

Sometimes inferences made at some specik time are valid at other times, too. In model-based diagnosis and monitoring as well as qualitative simulation inferences are often re-done although they have been performed previously. We propose a new methodfor sharing predictions done at different times, thus mutually cutting down prediction costs incurring at different times. Furthermore, we generalize the technique from ’sharing predictions across time' to ’sharing predictions across time and logical contexts'. Assumption-based truth maintenance is a form of sharing predictions across logical contexts. Because of the close connections to the ATMS we were able to use it as a means for implementation. We report empirical results on monitoring different con.Cgurations of ballast water tanks as used on offshore platforms and ships.


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