  This paper addresses the problem of resolving errors under uncertainty in a
rule-based system. A new approach has been developed that reformulates this
problem as a neural-network learning problem. The strength and the fundamental
limitations of this approach are explored and discussed. The main result is
that neural heuristics can be applied to solve some but not all problems in
rule-based systems.
