Parallel model-based diagnosis

Abstract

Model-Based Diagnosis (MBD) is a general-purpose computational approach to determine why a system under observation, e.g., an electronic circuit or a software program, does not behave as expected. MBD approaches utilize knowledge about the system's expected behavior if all of its components work correctly. In case of an unexpected behavior they systematically explore the possible reasons, i.e., diagnoses, for the misbehavior. Such diagnoses are determined through systematic or heuristic search procedures which often use MBD-specific rules to prune the search space. In this chapter we review approaches that rely on parallel or distributed computations to speed up the diagnostic reasoning process. Specifically, we focus on recent parallelization strategies that exploit the capabilities of modern multi-core computer architectures and report results from experimental evaluations to shed light on the speedups that can be achieved by parallelization for various MBD applications.