The Limits of Qualitative Simulation

Qualitative simulation is a key inference process in qualitative causal reasoning, In this paper, we present the QSIM algorithm, a new algorithm for qualitative simulation that generalizes the best features of rxisting algorithms, and allows direct comparisons among alternate approaches. QSIM is an efficient constraint-satisfaction algorithm that can follow either its standard semantics allowing the creation of new landmarks, or the { + , 0, -} semantics where 0 is the only landmark value, by changing a table of legal state-transitions. We argue that the QSIM semantics make more appropriate qualitative distinctions since the { + , 0 , } semantics can collapse the distinction among increasing, stable, or decreasing oscillation. We also show that (a) qualitative simulation algorithms can be proved to produce every actual behavior of the mechanism being modeled, but (b) existing qualitative simulation algorithms, because of their local points of view, can predict spurious behaviors not produced by any mechanism satisfying the structural description. These observations suggest specific types of care that must be taken in designing applications of qualitative causal reasoning systems, and in constructing and validating a knowledge base of mechanism descriptions.