Constraint propagation in a pathophysiologic causal network
Yong‐Bok Lee · 1986
The causal model approach to expert knowledge representation and reasoning, which is based on making the causal domain relationships explicit, is a focus of current research in expert systems. Currently existing model-based algorithms are, however, limited in the complexity of domains to which they can be applied. Recently, a semiquantitative simulation method integrated with a symbolic modeling approach based on functional and organizational primitives has been described. It has the ability to handle problems in complex domains involving nonlinear relationships between the causally related nodes. Its performance, however, requires the availability of the states used in the simulation. The term condition is used here to mean the specification of the values of all variables in the model at a given instant in time. These values, when then used for simulation, are initial in that they precede all simulated values. This thesis describes a new algorithm, called semi-quantitative inverse reasoning, for deriving a complete set of possible current state descriptions of an arbitrary complex causal model from partial specifications of the current state. Algorithms of constraint propagation by inference and hypothesis, hypothesis generation, and hypothesis conformation are developed to support the semi-quantitative inverse reasoning technique. Therefore in application to the medical domain, this technique can derive a complete set of primary diagnoses given medical data and an appropriate causal model.