Modeling and Diagnosing Problem-Solving System Behavior

A new component of a problem-solving system, called the diagnosis module (DM), that enables the system to reason about its own behavior is described. The aim of the diagnosis is to identify inappropriate control parameter settings or faulty hardware components as the causes of observed misbehavior. The problem-solving system being diagnosed is a distributed interpretation system, the distributed vehicle monitoring testbed (DVMT), which is based on a blackboard problem-solving architecture. The diagnosis module uses a causal model of the expected behavior of the DVMT to guide the diagnosis. Causal-model-based diagnosis is not new in AI. What is different is the application of this technique to the diagnosis of problem-solving system behavior. Problem-solving systems are characterized by the availability of the intermediate problem-solving state, the large amounts of data to process, and in some cases, the lack of absolute standards for behavior. New diagnostic techniques that exploit the availability of the intermediate problem-solving state and address the combinatorial problem arising from the large amount of data to analyze are described. A technique has also been developed, called comparative reasoning, for dealing with cases where no absolute standard for correct behavior is available. In such cases the diagnosis system selects its own "correct behavior criteria" from objects within the problem-solving system which did achieve some desired situation. The diagnosis module for the DVMT has been implemented and successfully identifies faults.