Controlling the complexity in model-based diagnosis

We present IDA — an incrementaldiagnosticalgorithm which computes minimal diagnoses from diagnoses, and not from conflicts. As a consequence of this, and by using different models, one can control the computational complexity. In particular, we show that by using a model of the normal behavior, the worst-case complexity of the algorithm to compute thek+1st minimal diagnosis isO(n^2k), wheren is the number of components. On the practical side, an experimental evaluation indicates that the algorithm can efficiently diagnose devices consisting of a few thousand components. We propose to use a hierarchy of models: first a structural model to compute all minimal diagnoses, then a normal behavior model to find the additional diagnoses if needed, and only then a fault model for their verification. IDA separates model interpretation from the search for minimal diagnoses in the sense that the model interpreter is replaceable. In particular, we show that in some domains it is advantageous to use the constraint logic programming system CLP(ß) instead of a logic programming system like Prolog.This is an extended version of the paper by Igor Mozeti, A polynomial-time algorithm for model-based diagnosis, which appears in theProc. European Conf. on Artificial Intelligence, ECAI-92, ed. B. Neumann (Wiley, 1992) pp. 729–733.