Truth Maintenance Under Uncertainty
This addresses error resolution in rule-based systems for AI applications, but it is incremental as it explores limitations without broad improvements.
The paper tackled the problem of resolving errors under uncertainty in rule-based systems by reformulating it as a neural-network learning problem, finding that neural heuristics can solve some but not all such problems.
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.