Results 1 
5 of
5
Qualitative and Quantitative Simulation: Bridging the Gap
 Artificial Intelligence
, 1997
"... Shortcomings of qualitative simulation and of quantitative simulation motivate combining them to do simulations exhibiting strengths of both. The resulting class of techniques is called semiquantitative simulation. One approach to semiquantitative simulation is to use numeric intervals to represe ..."
Abstract

Cited by 51 (1 self)
 Add to MetaCart
(Show Context)
Shortcomings of qualitative simulation and of quantitative simulation motivate combining them to do simulations exhibiting strengths of both. The resulting class of techniques is called semiquantitative simulation. One approach to semiquantitative simulation is to use numeric intervals to represent incomplete quantitative information. In this research we demonstrate semiquantitative simulation using intervals in an implemented semiquantitative simulator called Q3. Q3 progressively refines a qualitative simulation, providing increasingly specific quantitative predictions which can converge to a numerical simulation in the limit while retaining important correctness guarantees from qualitative and interval simulation techniques. Q3's simulations are based on a technique we call step size refinement. While a pure qualitative simulation has a very coarse step size, representing the state of a system trajectory at relatively few qualitatively distinct states, Q3 interpolates newly expl...
Monitoring And Diagnosis Of Continuous Dynamic Systems Using Semiquantitative Simulation
, 1992
"... ..."
Improving SemiQuantitative Reasoning by Landmark Approximation
, 1999
"... Many extensions to existing qualitative simulation packages allow combined use of available numeric and qualitative information. Some provide numeric integration across two qualitative states but none of them highlights numerically or semiquantitatively the qualitatively predicted time points. ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Many extensions to existing qualitative simulation packages allow combined use of available numeric and qualitative information. Some provide numeric integration across two qualitative states but none of them highlights numerically or semiquantitatively the qualitatively predicted time points. Moreover, high accuracy at time points is only achieved by a fine level of granularity across the entire time interval. This paper presents landmark approximation as a new methodology to improve semiquantitative predictions in qualitative simulation. We also present a transition table and a method to propagate semiquantitative values that is suitable for landmark approximation. These techniques have been implemented in a simulation engine that is based on qualitative reasoning techniques with a semiquantitative extension. We demonstrate that a semiquantitative approximation to landmarks improves the predictive power of the simulation tool even with a reduced number of inter...