Sound and complete qualitative simulation needs "quantitative" filtering
The AI methodology of qualitative reasoning furnishes useful tools to scientists and engineers who need to deal with incomplete system knowledge during design, analysis, or diagnosis tasks. Qualitative simulators have a theoretical soundness guarantee; they cannot "overlook" any concrete equation implied by their input. On the other hand, the basic qualitative simulation algorithms have been shown to suffer from the incompleteness problem; they may allow non-solutions of the input equation to appear in their output. The question of whether a simulator with purely qualitative input which never predicts spurious behaviors can ever be achieved by adding new filters to the existing algorithm has remained unanswered. In this paper, we show that, if such a "sound and complete" simulator exists, it will have to be able to handle numerical distinctions with such a high precision that it must contain a component that would better be called a "quantitative", rather than "qualitative" reasoner. This is due to the ability of the "pure" qualitative format to allow the exact representation of the members of a rich set of numbers.
qualitative simulation qualitative reasoning spurious predictions behavior filtering