Improving qualitative simulation with interval arithmetic and additional constraints

Conventional qualitative simulation methods supply good behavioral description for systems with qualitative description, but they also generate several behaviors and spurious solutions. In real-world applications, a more precise description of the system is needed to reduce the number of behaviors. This article improves the qualitative simulation algorithms by using interval arithmetic and by adding constraints. In engineering applications, quantitative knowledge that can be represented in terms of interval numbers is generally available. Interval arithmetic can combine this quantitative knowledge with qualitative to reduce the number of behaviors. Our approach is compared to the segment interval algorithm, and we show that this algorithm is not appropriate in qualitative physics because it removes good solutions. We also add new global filters, known as nondeterministic cycles and inflection point constraints, to reduce the number of solutions since quantitative knowledge cannot remove all the ambiguities. Some examples show the improvement made by our approach. © 1996 John Wiley & Sons, Inc.