Managing Uncertainty in Rule Based Cognitive Models
This work addresses uncertainty management in cognitive modeling for psychology and AI researchers, but it is incremental as it builds on prior findings.
The study tackled the problem of modeling uncertainty propagation in rule-based cognitive models by replicating and extending existing methods, resulting in a framework that uses maximum and minimum operations for antecedent evidence and scales certainty factors via multiplication.
An experiment replicated and extended recent findings on psychologically realistic ways of modeling propagation of uncertainty in rule based reasoning. Within a single production rule, the antecedent evidence can be summarized by taking the maximum of disjunctively connected antecedents and the minimum of conjunctively connected antecedents. The maximum certainty factor attached to each of the rule's conclusions can be sealed down by multiplication with this summarized antecedent certainty. Heckerman's modified certainty factor technique can be used to combine certainties for common conclusions across production rules.