Diagnosis Via Causal Reasoning: Paths of Interaction and the Locality Principle

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 3

Expert Systems

Interest has grown recently in developing expert systems that reason "from first principles", i.e., capable of the kind of problem solving exhibited by an engineer who can diagnose a malfunctioning device by reference to its schematics, even though he may never have seen that device before. In developing such a system for troubleshooting digital electronics, we have argued for the importance of pathways of causal interaction as a key concept. We have also suggested using a layered set of interaction paths as a way of constraining and guiding the diagnostic process. We report here on the implementation and use of these ideas. We show how they make it possible for our system to generate a few sharply constrained hypotheses in diagnosing a bridge fault. Abstracting from this example, we find a number of interesting general principles at work. We suggest that diagnosis can be viewed as the interaction of simulation and inference and we find that the concept of locality proves to be extremely useful in understanding why bridge faults are difficult to diagnose and why multiple representations are useful.